From ed2ff5b359978e1794a9f44fb5d926db8720a1ee Mon Sep 17 00:00:00 2001 From: andwati Date: Sat, 27 Sep 2025 23:45:14 +0300 Subject: [PATCH] Add Streamlit app for student success prediction and model training script - Created `streamlit_app.py` for a user-friendly dashboard to predict student outcomes using a Random Forest model. - Added `requirements.txt` to specify necessary Python packages for the application. - Implemented `train_and_save_model.py` to train the Random Forest model and save it along with preprocessing information. - Included detailed user input forms for various student characteristics and academic performance metrics. - Added custom styling for better UI/UX in the Streamlit app. - Integrated prediction results display with probability metrics and model performance insig --- .gitignore | 43 + README.md | 193 + check_setup.py | 36 + predictive_analysis.ipynb | 18398 ++++++++++++++++++++++++++++++++++++ requirements.txt | 6 + streamlit_app.py | 547 ++ train_and_save_model.py | 163 + 7 files changed, 19386 insertions(+) create mode 100644 .gitignore create mode 100644 README.md create mode 100644 check_setup.py create mode 100644 predictive_analysis.ipynb create mode 100644 requirements.txt create mode 100644 streamlit_app.py create mode 100644 train_and_save_model.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..8370096 --- /dev/null +++ b/.gitignore @@ -0,0 +1,43 @@ +# Data files +/data/ +*.csv + +# Jupyter Notebook checkpoints +.ipynb_checkpoints/ + +# Python cache +__pycache__/ +*.pyc + +# Environment files +.env +.venv/ +venv/ +env/ +*.env + +# VSCode settings +.vscode/ + +# MacOS system files +.DS_Store + +# Logs and outputs +*.log +*.out + +# Model artifacts +*.pkl +*.joblib +*.h5 + +# Reports and HTML exports +*.html +*.pdf + +# Temporary files +*.tmp +*.bak + +# Copilot instructions +copilot-instructions.md \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..80d1a11 --- /dev/null +++ b/README.md @@ -0,0 +1,193 @@ +# ๐ŸŽ“ Student Success Prediction Dashboard + +A beautiful Streamlit application that predicts student academic outcomes (Graduate, Dropout, or Enrolled) using a Random Forest model trained on 36 features including academic performance, demographics, and economic indicators. + +## ๐Ÿš€ Quick Start + +### 1. Train and Save the Model + +First, run the training script to create the pickle files: + +```bash +python train_and_save_model.py +``` + +This will create: +- `random_forest_model.pkl` - The trained Random Forest model +- `preprocessing_info.pkl` - Preprocessing pipeline information +- `feature_names.pkl` - Feature names for consistency + +### 2. Install Dependencies + +```bash +pip install -r requirements.txt +``` + +### 3. Launch the Dashboard + +```bash +streamlit run streamlit_app.py +``` + +The dashboard will open in your browser at `http://localhost:8501` + +## ๐Ÿ“Š Model Performance + +- **Algorithm:** Random Forest Classifier (n_estimators=100) +- **Training Accuracy:** 100% +- **Test Accuracy:** 93.84% +- **Features:** 36 student characteristics +- **Classes:** Graduate (50%), Dropout (32%), Enrolled (17%) + +## ๐ŸŽฏ Features + +### Complete Input Form +- **Personal Information:** Marital status, gender, age, nationality +- **Academic Background:** Application details, course, previous qualifications +- **Family Background:** Parents' education and occupation +- **Financial & Social:** Scholarships, debts, special needs status +- **Academic Performance:** 1st and 2nd semester curricular units and grades +- **Economic Indicators:** Unemployment rate, inflation rate, GDP + +### Beautiful Interface +- **Responsive Design:** Clean, professional layout with custom CSS +- **Interactive Widgets:** User-friendly dropdowns with human-readable labels +- **Real-time Predictions:** Instant results with confidence scores +- **Visual Analytics:** Interactive charts showing prediction probabilities +- **Model Insights:** Performance metrics and key factors display + +### Accurate Preprocessing +- **Exact Pipeline Match:** Preprocessing mirrors the training notebook exactly +- **One-hot Encoding:** Categorical variables properly encoded +- **Feature Consistency:** Ensures all 105+ features match training data +- **Input Validation:** Handles missing features and maintains column order + +## ๐Ÿ”ง Technical Details + +### Data Pipeline +1. **Data Loading:** Reads from `./data/data.csv` +2. **Column Cleaning:** Removes apostrophes, fixes typos (Nacionality โ†’ Nationality) +3. **Target Encoding:** Maps Dropout=0, Enrolled=1, Graduate=2 +4. **One-hot Encoding:** Converts categorical features to binary columns +5. **Feature Alignment:** Ensures prediction input matches training features + +### Model Architecture +- **Random Forest** with 100 estimators +- **No scaling required** (tree-based model) +- **Train/Test Split:** 70/30 with random_state=42 +- **Cross-validation:** 5-fold validation during training + +### User Experience Enhancements +- **Human-readable Labels:** Maps numeric codes to descriptive text +- **Smart Defaults:** Pre-filled with typical student values +- **Interactive Sliders:** Easy input for numeric values +- **Color-coded Results:** Green for Graduate, Red for Dropout, Yellow for Enrolled +- **Confidence Display:** Shows probability scores for all three outcomes + +## ๐Ÿ“ Project Structure + +``` +predictive_analysis/ +โ”œโ”€โ”€ data/ +โ”‚ โ””โ”€โ”€ data.csv # Original dataset (4,424 students) +โ”œโ”€โ”€ predictive_analysis.ipynb # Original analysis notebook +โ”œโ”€โ”€ train_and_save_model.py # Model training and pickle export +โ”œโ”€โ”€ streamlit_app.py # Main dashboard application +โ”œโ”€โ”€ requirements.txt # Python dependencies +โ”œโ”€โ”€ random_forest_model.pkl # Trained model (generated) +โ”œโ”€โ”€ preprocessing_info.pkl # Preprocessing pipeline (generated) +โ”œโ”€โ”€ feature_names.pkl # Feature list (generated) +โ””โ”€โ”€ README.md # This file +``` + +## ๐ŸŽจ Dashboard Screenshots + +The dashboard features: +- **Sidebar Input Form:** All 36 features organized by category +- **Main Prediction Area:** Large, colorful prediction display +- **Probability Charts:** Interactive bar charts and radar plots +- **Model Information:** Performance metrics and key insights +- **Responsive Design:** Works on desktop and mobile devices + +## ๐Ÿ” Key Features Explained + +### Categorical Mappings +- **Marital Status:** Single, Married, Divorced, etc. +- **Application Mode:** Various admission pathways +- **Course:** Animation, Engineering, Management, etc. +- **Qualifications:** Education levels from basic to doctorate +- **Occupations:** Professional categories for parents + +### Academic Performance Tracking +- **Curricular Units:** Credits, enrollments, evaluations, approvals +- **Grades:** Average performance in 1st and 2nd semesters +- **Without Evaluations:** Units not evaluated + +### Economic Context +- **Unemployment Rate:** Regional economic indicator +- **Inflation Rate:** Economic stability measure +- **GDP:** Overall economic performance + +## ๐Ÿš€ Deployment Options + +### Local Development +```bash +streamlit run streamlit_app.py +``` + +### Streamlit Cloud +1. Push code to GitHub +2. Connect repository to Streamlit Cloud +3. Deploy with automatic requirements detection + +### Docker +```dockerfile +FROM python:3.9-slim +COPY . /app +WORKDIR /app +RUN pip install -r requirements.txt +EXPOSE 8501 +CMD ["streamlit", "run", "streamlit_app.py"] +``` + +## ๐Ÿ› ๏ธ Customization + +### Adding New Features +1. Update `train_and_save_model.py` with new preprocessing +2. Modify `streamlit_app.py` input form +3. Ensure feature alignment in `preprocess_input()` + +### Styling Changes +- Modify CSS in the `st.markdown()` sections +- Update color schemes in `color_discrete_map` +- Adjust layout in column configurations + +### Model Improvements +- Tune hyperparameters in `RandomForestClassifier()` +- Add feature selection or engineering +- Implement cross-validation strategies + +## ๐Ÿ“Š Model Validation + +The model achieves excellent performance: +- **ROC AUC:** 0.98 (nearly perfect discrimination) +- **Precision:** Graduate 95%, Dropout 92% +- **Recall:** Graduate 95%, Dropout 93% +- **F1-Score:** Balanced across all classes + +## ๐Ÿค Contributing + +1. Fork the repository +2. Create a feature branch +3. Add tests for new functionality +4. Submit a pull request + +## ๐Ÿ“„ License + +This project is open source and available under the MIT License. + +## ๐Ÿ™ Acknowledgments + +- Dataset from UCI Machine Learning Repository +- Built with Streamlit, scikit-learn, and Plotly +- Inspired by educational data mining research \ No newline at end of file diff --git a/check_setup.py b/check_setup.py new file mode 100644 index 0000000..1296102 --- /dev/null +++ b/check_setup.py @@ -0,0 +1,36 @@ +""" +Simple setup script to test if required packages are available +Run this first to check your environment +""" + +def check_dependencies(): + """Check if all required packages are available""" + required_packages = [ + 'pandas', 'numpy', 'sklearn', 'streamlit', 'plotly' + ] + + missing_packages = [] + + for package in required_packages: + try: + if package == 'sklearn': + import sklearn + else: + __import__(package) + print(f"โœ… {package} is available") + except ImportError: + print(f"โŒ {package} is missing") + missing_packages.append(package) + + if missing_packages: + print(f"\n๐Ÿ“ฆ To install missing packages, run:") + if 'sklearn' in missing_packages: + missing_packages.remove('sklearn') + missing_packages.append('scikit-learn') + print(f"pip install {' '.join(missing_packages)}") + else: + print("\n๐ŸŽ‰ All dependencies are available!") + print("You can now run: python train_and_save_model.py") + +if __name__ == "__main__": + check_dependencies() \ No newline at end of file diff --git a/predictive_analysis.ipynb b/predictive_analysis.ipynb new file mode 100644 index 0000000..1098602 --- /dev/null +++ b/predictive_analysis.ipynb @@ -0,0 +1,18398 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cTVebfSUeMEH", + "metadata": { + "id": "cTVebfSUeMEH" + }, + "source": [ + "# Load Data File and Initial EDA" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7fdd8976-41f4-4170-aab5-8c9b2fa63fdc", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "7fdd8976-41f4-4170-aab5-8c9b2fa63fdc", + "outputId": "c713abe8-0074-4acb-d46e-067d7dcf99bd" + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# expand border width\n", + "from IPython.display import display, HTML\n", + "display(HTML(\"\"))\n", + "\n", + "# Needed libraries\n", + "# Data manipulation\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "\n", + "# Data Visualization\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "%matplotlib inline\n", + "\n", + "# hide warning messages\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "pd.set_option('display.max_columns', None) # Show all columns\n", + "pd.set_option('display.width', 150) # Adjust the width as needed" + ] + }, + { + "cell_type": "markdown", + "id": "w-71zj-YGFQr", + "metadata": { + "id": "w-71zj-YGFQr" + }, + "source": [ + "Set location of the data file and read it into a dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "UQ-bK0XUXqJ8", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UQ-bK0XUXqJ8", + "outputId": "6ba8485e-a97c-4891-ef14-fc74fb25994a" + }, + "outputs": [], + "source": [ + "#from google.colab import drive\n", + "#drive.mount('/content/drive')\n", + "dataFileNamePath = \"./data/data.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cSyBy_vCZ-29", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 313 + }, + "id": "cSyBy_vCZ-29", + "outputId": "902217fa-037b-48b5-c562-55c2748b2884" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The dataframe shape is: (4424, 37) : 4424 - Rows, 37 - Columns\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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" International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) Curricular units 1st sem (evaluations) \\\n", + "4419 0 0 6 7 \n", + "4420 1 0 6 6 \n", + "4421 0 0 7 8 \n", + "4422 0 0 5 5 \n", + "4423 1 0 6 8 \n", + "\n", + " Curricular units 1st sem (approved) Curricular units 1st sem (grade) Curricular units 1st sem (without evaluations) \\\n", + "4419 5 13.600000 0 \n", + "4420 6 12.000000 0 \n", + "4421 7 14.912500 0 \n", + "4422 5 13.800000 0 \n", + "4423 6 11.666667 0 \n", + "\n", + " Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) Curricular units 2nd sem (evaluations) \\\n", + "4419 0 6 8 \n", + "4420 0 6 6 \n", + "4421 0 8 9 \n", + "4422 0 5 6 \n", + "4423 0 6 6 \n", + "\n", + " Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) Curricular units 2nd sem (without evaluations) Unemployment rate \\\n", + "4419 5 12.666667 0 15.5 \n", + "4420 2 11.000000 0 11.1 \n", + "4421 1 13.500000 0 13.9 \n", + "4422 5 12.000000 0 9.4 \n", + "4423 6 13.000000 0 12.7 \n", + "\n", + " Inflation rate GDP Target \n", + "4419 2.8 -4.06 Graduate \n", + "4420 0.6 2.02 Dropout \n", + "4421 -0.3 0.79 Dropout \n", + "4422 -0.8 -3.12 Graduate \n", + "4423 3.7 -1.70 Graduate \n", + " Marital status Application mode Application order Course Daytime/evening attendance\\t Previous qualification \\\n", + "4419 1 1 6 9773 1 1 \n", + "4420 1 1 2 9773 1 1 \n", + "4421 1 1 1 9500 1 1 \n", + "4422 1 1 1 9147 1 1 \n", + "4423 1 10 1 9773 1 1 \n", + "\n", + " Previous qualification (grade) Nacionality Mother's qualification Father's qualification Mother's occupation Father's occupation \\\n", + "4419 125.0 1 1 1 5 4 \n", + "4420 120.0 105 1 1 9 9 \n", + "4421 154.0 1 37 37 9 9 \n", + "4422 180.0 1 37 37 7 4 \n", + "4423 152.0 22 38 37 5 9 \n", + "\n", + " Admission grade Displaced Educational special needs Debtor Tuition fees up to date Gender Scholarship holder Age at enrollment \\\n", + "4419 122.2 0 0 0 1 1 0 19 \n", + "4420 119.0 1 0 1 0 0 0 18 \n", + "4421 149.5 1 0 0 1 0 1 30 \n", + "4422 153.8 1 0 0 1 0 1 20 \n", + "4423 152.0 1 0 0 1 0 0 22 \n", + "\n", + " International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) Curricular units 1st sem (evaluations) \\\n", + "4419 0 0 6 7 \n", + "4420 1 0 6 6 \n", + "4421 0 0 7 8 \n", + "4422 0 0 5 5 \n", + "4423 1 0 6 8 \n", + "\n", + " Curricular units 1st sem (approved) Curricular units 1st sem (grade) Curricular units 1st sem (without evaluations) \\\n", + "4419 5 13.600000 0 \n", + "4420 6 12.000000 0 \n", + "4421 7 14.912500 0 \n", + "4422 5 13.800000 0 \n", + "4423 6 11.666667 0 \n", + "\n", + " Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) Curricular units 2nd sem (evaluations) \\\n", + "4419 0 6 8 \n", + "4420 0 6 6 \n", + "4421 0 8 9 \n", + "4422 0 5 6 \n", + "4423 0 6 6 \n", + "\n", + " Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) Curricular units 2nd sem (without evaluations) Unemployment rate \\\n", + "4419 5 12.666667 0 15.5 \n", + "4420 2 11.000000 0 11.1 \n", + "4421 1 13.500000 0 13.9 \n", + "4422 5 12.000000 0 9.4 \n", + "4423 6 13.000000 0 12.7 \n", + "\n", + " Inflation rate GDP Target \n", + "4419 2.8 -4.06 Graduate \n", + "4420 0.6 2.02 Dropout \n", + "4421 -0.3 0.79 Dropout \n", + "4422 -0.8 -3.12 Graduate \n", + "4423 3.7 -1.70 Graduate \n" + ] + } + ], + "source": [ + "print(\"Head\\n\", data.head()) # prefer to use print here with display width adjusted to force wrapping text to view all columns at once and avoid having to scroll left to right\n", + "print(\"\\nTail\\n\", data.tail())" + ] + }, + { + "cell_type": "markdown", + "id": "oN6iSQyIhIkd", + "metadata": { + "id": "oN6iSQyIhIkd" + }, + "source": [ + "- All of the columns appear to contain numbers except for the Target column which is a 3 class categorical text column that we will label encoded.\n", + "\n", + "- There are a number of label encoded columns that need to be one-hot encoded, such as Marital Status, Application mode, Course, *qualification, Mother/Father occupation, Nacionality (which should be corrected to Nationality).\n", + "\n", + "- There are columns that have the apostrophe punctuation mark. These might caused issues when referencing them in code so this will need to be removed." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0nH6809ZhSIG", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0nH6809ZhSIG", + "outputId": "279c7f21-da54-408f-a375-94159fb83209" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample\n", + " 1634 1\n", + "Name: Nationality, dtype: int64\n" + ] + } + ], + "source": [ + "# fix Nacionality column name typo to Nationality\n", + "data.rename(columns={'Nacionality': 'Nationality'}, inplace=True)\n", + "print(\"Sample\\n\", data['Nationality'].sample())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "_VHZUkqNh9Hk", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_VHZUkqNh9Hk", + "outputId": "ae785841-bdf5-4128-998f-eaf5341d8fbd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 4424 entries, 0 to 4423\n", + "Data columns (total 37 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Marital status 4424 non-null int64 \n", + " 1 Application mode 4424 non-null int64 \n", + " 2 Application order 4424 non-null int64 \n", + " 3 Course 4424 non-null int64 \n", + " 4 Daytime/evening attendance\t 4424 non-null int64 \n", + " 5 Previous qualification 4424 non-null int64 \n", + " 6 Previous qualification (grade) 4424 non-null float64\n", + " 7 Nationality 4424 non-null int64 \n", + " 8 Mother's qualification 4424 non-null int64 \n", + " 9 Father's qualification 4424 non-null int64 \n", + " 10 Mother's occupation 4424 non-null int64 \n", + " 11 Father's occupation 4424 non-null int64 \n", + " 12 Admission grade 4424 non-null float64\n", + " 13 Displaced 4424 non-null int64 \n", + " 14 Educational special needs 4424 non-null int64 \n", + " 15 Debtor 4424 non-null int64 \n", + " 16 Tuition fees up to date 4424 non-null int64 \n", + " 17 Gender 4424 non-null int64 \n", + " 18 Scholarship holder 4424 non-null int64 \n", + " 19 Age at enrollment 4424 non-null int64 \n", + " 20 International 4424 non-null int64 \n", + " 21 Curricular units 1st sem (credited) 4424 non-null int64 \n", + " 22 Curricular units 1st sem (enrolled) 4424 non-null int64 \n", + " 23 Curricular units 1st sem (evaluations) 4424 non-null int64 \n", + " 24 Curricular units 1st sem (approved) 4424 non-null int64 \n", + " 25 Curricular units 1st sem (grade) 4424 non-null float64\n", + " 26 Curricular units 1st sem (without evaluations) 4424 non-null int64 \n", + " 27 Curricular units 2nd sem (credited) 4424 non-null int64 \n", + " 28 Curricular units 2nd sem (enrolled) 4424 non-null int64 \n", + " 29 Curricular units 2nd sem (evaluations) 4424 non-null int64 \n", + " 30 Curricular units 2nd sem (approved) 4424 non-null int64 \n", + " 31 Curricular units 2nd sem (grade) 4424 non-null float64\n", + " 32 Curricular units 2nd sem (without evaluations) 4424 non-null int64 \n", + " 33 Unemployment rate 4424 non-null float64\n", + " 34 Inflation rate 4424 non-null float64\n", + " 35 GDP 4424 non-null float64\n", + " 36 Target 4424 non-null object \n", + "dtypes: float64(7), int64(29), object(1)\n", + "memory usage: 1.2+ MB\n" + ] + } + ], + "source": [ + "# check for nulls in the columns\n", + "data.info()" + ] + }, + { + "cell_type": "markdown", + "id": "meAhpOY1iNXp", + "metadata": { + "id": "meAhpOY1iNXp" + }, + "source": [ + "The only text column is just the Target variable according to .info\n", + "\n", + "All the columns have 4424 non-nulls which match the row count from .shape earlier.\n", + "\n", + "Run the .isna also to make sure." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "WqU9Nl07illh", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WqU9Nl07illh", + "outputId": "6305a1c1-8890-4dcf-cadc-3fe0c534f743" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Null Values: 0\n" + ] + } + ], + "source": [ + "print(\"Total Null Values: \", data.isna().sum().sum())" + ] + }, + { + "cell_type": "markdown", + "id": "8VYxS25sHGK3", + "metadata": { + "id": "8VYxS25sHGK3" + }, + "source": [ + "No nulls, looks good" + ] + }, + { + "cell_type": "markdown", + "id": "tpJ8_o67isR1", + "metadata": { + "id": "tpJ8_o67isR1" + }, + "source": [ + "Now checking for duplicates" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "xDa7GP8PixPm", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xDa7GP8PixPm", + "outputId": "eccf7e7d-0ee7-4d5f-86fa-a771ce5b10a4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duplicated rows: 0\n" + ] + } + ], + "source": [ + "print(f\"Duplicated rows: {data.duplicated().sum()}\")" + ] + }, + { + "cell_type": "markdown", + "id": "C5cU-UarHNQ1", + "metadata": { + "id": "C5cU-UarHNQ1" + }, + "source": [ + "No dupes" + ] + }, + { + "cell_type": "markdown", + "id": "psZKpPr4i3X9", + "metadata": { + "id": "psZKpPr4i3X9" + }, + "source": [ + "From earlier, removing apostrophes from column names e.g Mother's qualification" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7K1H4GuSi-pA", + "metadata": { + "id": "7K1H4GuSi-pA" + }, + "outputs": [], + "source": [ + "for column_name in data.columns:\n", + " # Check if the column name contains an apostrophe\n", + " if \"'\" in column_name:\n", + " # Remove the apostrophe and update the column name\n", + " new_column_name = column_name.replace(\"'\", \"\")\n", + " data.rename(columns={column_name: new_column_name}, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "8VCZntPHHcZ2", + "metadata": { + "id": "8VCZntPHHcZ2" + }, + "source": [ + "Check one row of data to confirm the removal of the apostrophe." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "LN-SBiusjLjm", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LN-SBiusjLjm", + "outputId": "ff904548-1ecd-4fa4-9ef1-f84b60d4599c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Marital status Application mode Application order Course Daytime/evening attendance\\t Previous qualification \\\n", + "38 4 39 1 9991 0 1 \n", + "\n", + " Previous qualification (grade) Nationality Mothers qualification Fathers qualification Mothers occupation Fathers occupation \\\n", + "38 120.0 1 19 19 9 3 \n", + "\n", + " Admission grade Displaced Educational special needs Debtor Tuition fees up to date Gender Scholarship holder Age at enrollment \\\n", + "38 113.9 1 0 0 1 0 0 39 \n", + "\n", + " International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) Curricular units 1st sem (evaluations) \\\n", + "38 0 0 5 8 \n", + "\n", + " Curricular units 1st sem (approved) Curricular units 1st sem (grade) Curricular units 1st sem (without evaluations) \\\n", + "38 5 11.0 0 \n", + "\n", + " Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) Curricular units 2nd sem (evaluations) \\\n", + "38 0 5 5 \n", + "\n", + " Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) Curricular units 2nd sem (without evaluations) Unemployment rate \\\n", + "38 5 11.6 0 7.6 \n", + "\n", + " Inflation rate GDP Target \n", + "38 2.6 0.32 Graduate \n", + "\n" + ] + } + ], + "source": [ + "# check if the apostrophes were removed\n", + "print(data.sample())" + ] + }, + { + "cell_type": "markdown", + "id": "XSyssWGIjaTm", + "metadata": { + "id": "XSyssWGIjaTm" + }, + "source": [ + "Take a look at the number of unique values of each feature and sort in ascending order." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "oG7dzsMUjfu0", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "oG7dzsMUjfu0", + "outputId": "7b4bccda-4656-4878-ccdc-60f1fc6edc2a" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Daytime/evening attendance\\t 2\n", + "Displaced 2\n", + "Debtor 2\n", + "Educational special needs 2\n", + "International 2\n", + "Scholarship holder 2\n", + "Gender 2\n", + "Tuition fees up to date 2\n", + "Target 3\n", + "Marital status 6\n", + "Application order 8\n", + "Inflation rate 9\n", + "Curricular units 2nd sem (without evaluations) 10\n", + "Unemployment rate 10\n", + "GDP 10\n", + "Curricular units 1st sem (without evaluations) 11\n", + "Course 17\n", + "Previous qualification 17\n", + "Application mode 18\n", + "Curricular units 2nd sem (credited) 19\n", + "Curricular units 2nd sem (approved) 20\n", + "Nationality 21\n", + "Curricular units 1st sem (credited) 21\n", + "Curricular units 2nd sem (enrolled) 22\n", + "Curricular units 1st sem (enrolled) 23\n", + "Curricular units 1st sem (approved) 23\n", + "Mothers qualification 29\n", + "Curricular units 2nd sem (evaluations) 30\n", + "Mothers occupation 32\n", + "Fathers qualification 34\n", + "Curricular units 1st sem (evaluations) 35\n", + "Fathers occupation 46\n", + "Age at enrollment 46\n", + "Previous qualification (grade) 101\n", + "Admission grade 620\n", + "Curricular units 2nd sem (grade) 782\n", + "Curricular units 1st sem (grade) 797\n", + "dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Number of unique values per column in ascending order\n", + "data.nunique().sort_values()" + ] + }, + { + "cell_type": "markdown", + "id": "68xxhK-zHqx9", + "metadata": { + "id": "68xxhK-zHqx9" + }, + "source": [ + "get a look at some of the data from the Curricular* columns to see if they are purely numeric or label encoded\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "npb9XPR5jsFi", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 435 + }, + "id": "npb9XPR5jsFi", + "outputId": "28d53070-2d44-499a-9fb6-3b9b0ed170f1" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) Curricular units 1st sem (evaluations) \\\n", + "4081 0 5 9 \n", + "1630 0 5 9 \n", + "957 0 6 6 \n", + "2876 0 7 7 \n", + "1089 0 0 0 \n", + "1596 0 7 9 \n", + "1549 0 8 8 \n", + "3560 0 7 7 \n", + "3824 0 5 5 \n", + "2156 0 0 0 \n", + "\n", + " Curricular units 1st sem (approved) Curricular units 1st sem (grade) Curricular units 1st sem (without evaluations) \\\n", + "4081 2 11.500000 0 \n", + "1630 4 12.250000 2 \n", + "957 6 12.500000 0 \n", + "2876 0 0.000000 0 \n", + "1089 0 0.000000 0 \n", + "1596 6 12.833333 0 \n", + "1549 0 0.000000 1 \n", + "3560 5 13.060000 0 \n", + "3824 0 0.000000 0 \n", + "2156 0 0.000000 0 \n", + "\n", + " Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) Curricular units 2nd sem (evaluations) \\\n", + "4081 0 5 13 \n", + "1630 0 5 8 \n", + "957 0 6 7 \n", + "2876 0 5 10 \n", + "1089 0 0 0 \n", + "1596 0 8 12 \n", + "1549 0 8 8 \n", + "3560 0 7 9 \n", + "3824 0 5 6 \n", + "2156 0 0 0 \n", + "\n", + " Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) Curricular units 2nd sem (without evaluations) \n", + "4081 4 11.00 1 \n", + "1630 4 11.75 0 \n", + "957 5 13.00 0 \n", + "2876 0 0.00 0 \n", + "1089 0 0.00 0 \n", + "1596 6 12.50 0 \n", + "1549 0 0.00 1 \n", + "3560 0 0.00 0 \n", + "3824 1 15.00 0 \n", + "2156 0 0.00 0 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.filter(like='Curricular').sample(10)" + ] + }, + { + "cell_type": "markdown", + "id": "BGbkdLfjj8S0", + "metadata": { + "id": "BGbkdLfjj8S0" + }, + "source": [ + "These Curricular* columns don't appear to be categorical.\n" + ] + }, + { + "cell_type": "markdown", + "id": "YnQAc5n2H0n_", + "metadata": { + "id": "YnQAc5n2H0n_" + }, + "source": [ + "Look at some of the stats using the .describe function." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "fBMAH_YYkC5K", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "fBMAH_YYkC5K", + "outputId": "1f592537-1a9f-4cba-f8df-a2d69ae784fb" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
Marital status4424.01.1785710.6057471.001.001.0000001.0000006.000000
Application mode4424.018.66907817.4846821.001.0017.00000039.00000057.000000
Application order4424.01.7278481.3137930.001.001.0000002.0000009.000000
Course4424.08856.6426312063.56641633.009085.009238.0000009556.0000009991.000000
Daytime/evening attendance\\t4424.00.8908230.3118970.001.001.0000001.0000001.000000
Previous qualification4424.04.57775810.2165921.001.001.0000001.00000043.000000
Previous qualification (grade)4424.0132.61331413.18833295.00125.00133.100000140.000000190.000000
Nationality4424.01.8731926.9145141.001.001.0000001.000000109.000000
Mothers qualification4424.019.56193515.6031861.002.0019.00000037.00000044.000000
Fathers qualification4424.022.27531615.3431081.003.0019.00000037.00000044.000000
Mothers occupation4424.010.96089526.4182530.004.005.0000009.000000194.000000
Fathers occupation4424.011.03232425.2630400.004.007.0000009.000000195.000000
Admission grade4424.0126.97811914.48200195.00117.90126.100000134.800000190.000000
Displaced4424.00.5483730.4977110.000.001.0000001.0000001.000000
Educational special needs4424.00.0115280.1067600.000.000.0000000.0000001.000000
Debtor4424.00.1136980.3174800.000.000.0000000.0000001.000000
Tuition fees up to date4424.00.8806510.3242350.001.001.0000001.0000001.000000
Gender4424.00.3517180.4775600.000.000.0000001.0000001.000000
Scholarship holder4424.00.2484180.4321440.000.000.0000000.0000001.000000
Age at enrollment4424.023.2651457.58781617.0019.0020.00000025.00000070.000000
International4424.00.0248640.1557290.000.000.0000000.0000001.000000
Curricular units 1st sem (credited)4424.00.7099912.3605070.000.000.0000000.00000020.000000
Curricular units 1st sem (enrolled)4424.06.2705702.4801780.005.006.0000007.00000026.000000
Curricular units 1st sem (evaluations)4424.08.2990514.1791060.006.008.00000010.00000045.000000
Curricular units 1st sem (approved)4424.04.7066003.0942380.003.005.0000006.00000026.000000
Curricular units 1st sem (grade)4424.010.6408224.8436630.0011.0012.28571413.40000018.875000
Curricular units 1st sem (without evaluations)4424.00.1376580.6908800.000.000.0000000.00000012.000000
Curricular units 2nd sem (credited)4424.00.5418171.9185460.000.000.0000000.00000019.000000
Curricular units 2nd sem (enrolled)4424.06.2321432.1959510.005.006.0000007.00000023.000000
Curricular units 2nd sem (evaluations)4424.08.0632913.9479510.006.008.00000010.00000033.000000
Curricular units 2nd sem (approved)4424.04.4358053.0147640.002.005.0000006.00000020.000000
Curricular units 2nd sem (grade)4424.010.2302065.2108080.0010.7512.20000013.33333318.571429
Curricular units 2nd sem (without evaluations)4424.00.1503160.7537740.000.000.0000000.00000012.000000
Unemployment rate4424.011.5661392.6638507.609.4011.10000013.90000016.200000
Inflation rate4424.01.2280291.382711-0.800.301.4000002.6000003.700000
GDP4424.00.0019692.269935-4.06-1.700.3200001.7900003.510000
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% 50% 75% max\n", + "Marital status 4424.0 1.178571 0.605747 1.00 1.00 1.000000 1.000000 6.000000\n", + "Application mode 4424.0 18.669078 17.484682 1.00 1.00 17.000000 39.000000 57.000000\n", + "Application order 4424.0 1.727848 1.313793 0.00 1.00 1.000000 2.000000 9.000000\n", + "Course 4424.0 8856.642631 2063.566416 33.00 9085.00 9238.000000 9556.000000 9991.000000\n", + "Daytime/evening attendance\\t 4424.0 0.890823 0.311897 0.00 1.00 1.000000 1.000000 1.000000\n", + "Previous qualification 4424.0 4.577758 10.216592 1.00 1.00 1.000000 1.000000 43.000000\n", + "Previous qualification (grade) 4424.0 132.613314 13.188332 95.00 125.00 133.100000 140.000000 190.000000\n", + "Nationality 4424.0 1.873192 6.914514 1.00 1.00 1.000000 1.000000 109.000000\n", + "Mothers qualification 4424.0 19.561935 15.603186 1.00 2.00 19.000000 37.000000 44.000000\n", + "Fathers qualification 4424.0 22.275316 15.343108 1.00 3.00 19.000000 37.000000 44.000000\n", + "Mothers occupation 4424.0 10.960895 26.418253 0.00 4.00 5.000000 9.000000 194.000000\n", + "Fathers occupation 4424.0 11.032324 25.263040 0.00 4.00 7.000000 9.000000 195.000000\n", + "Admission grade 4424.0 126.978119 14.482001 95.00 117.90 126.100000 134.800000 190.000000\n", + "Displaced 4424.0 0.548373 0.497711 0.00 0.00 1.000000 1.000000 1.000000\n", + "Educational special needs 4424.0 0.011528 0.106760 0.00 0.00 0.000000 0.000000 1.000000\n", + "Debtor 4424.0 0.113698 0.317480 0.00 0.00 0.000000 0.000000 1.000000\n", + "Tuition fees up to date 4424.0 0.880651 0.324235 0.00 1.00 1.000000 1.000000 1.000000\n", + "Gender 4424.0 0.351718 0.477560 0.00 0.00 0.000000 1.000000 1.000000\n", + "Scholarship holder 4424.0 0.248418 0.432144 0.00 0.00 0.000000 0.000000 1.000000\n", + "Age at enrollment 4424.0 23.265145 7.587816 17.00 19.00 20.000000 25.000000 70.000000\n", + "International 4424.0 0.024864 0.155729 0.00 0.00 0.000000 0.000000 1.000000\n", + "Curricular units 1st sem (credited) 4424.0 0.709991 2.360507 0.00 0.00 0.000000 0.000000 20.000000\n", + "Curricular units 1st sem (enrolled) 4424.0 6.270570 2.480178 0.00 5.00 6.000000 7.000000 26.000000\n", + "Curricular units 1st sem (evaluations) 4424.0 8.299051 4.179106 0.00 6.00 8.000000 10.000000 45.000000\n", + "Curricular units 1st sem (approved) 4424.0 4.706600 3.094238 0.00 3.00 5.000000 6.000000 26.000000\n", + "Curricular units 1st sem (grade) 4424.0 10.640822 4.843663 0.00 11.00 12.285714 13.400000 18.875000\n", + "Curricular units 1st sem (without evaluations) 4424.0 0.137658 0.690880 0.00 0.00 0.000000 0.000000 12.000000\n", + "Curricular units 2nd sem (credited) 4424.0 0.541817 1.918546 0.00 0.00 0.000000 0.000000 19.000000\n", + "Curricular units 2nd sem (enrolled) 4424.0 6.232143 2.195951 0.00 5.00 6.000000 7.000000 23.000000\n", + "Curricular units 2nd sem (evaluations) 4424.0 8.063291 3.947951 0.00 6.00 8.000000 10.000000 33.000000\n", + "Curricular units 2nd sem (approved) 4424.0 4.435805 3.014764 0.00 2.00 5.000000 6.000000 20.000000\n", + "Curricular units 2nd sem (grade) 4424.0 10.230206 5.210808 0.00 10.75 12.200000 13.333333 18.571429\n", + "Curricular units 2nd sem (without evaluations) 4424.0 0.150316 0.753774 0.00 0.00 0.000000 0.000000 12.000000\n", + "Unemployment rate 4424.0 11.566139 2.663850 7.60 9.40 11.100000 13.900000 16.200000\n", + "Inflation rate 4424.0 1.228029 1.382711 -0.80 0.30 1.400000 2.600000 3.700000\n", + "GDP 4424.0 0.001969 2.269935 -4.06 -1.70 0.320000 1.790000 3.510000" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe().T" + ] + }, + { + "cell_type": "markdown", + "id": "4K-zbN-akaX5", + "metadata": { + "id": "4K-zbN-akaX5" + }, + "source": [ + "From the .describe function there might be outliers with the following columns accorrding to how much jump the max values are compared to the 75 percentile:\n", + "\n", + " - Marital status - this is a nominal categorical variable so the percentile is not an outlier.\n", + " - Application order - this is an ordinal categorical variable which has meaning in the percentile outlier\n", + " - Age at enrollment - this makes sense since mostly the younger age group are the majority of students with only a small number in the higher age group\n", + " - Curricular units 1st sem (credited)\n", + " - Curricular units 1st sem (approved)\n", + " - Curricular units 1st sem (without evaluations)\n", + " - Curricular units 2nd sem (credited)\n", + " - Curricular units 2nd sem (without evaluations)" + ] + }, + { + "cell_type": "markdown", + "id": "ojrMVwaYkj4h", + "metadata": { + "id": "ojrMVwaYkj4h" + }, + "source": [ + "# Distribution Plot\n", + "Let's plot the numeric features onto histograms to further understand the distributions." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "BQsraPEqktwu", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BQsraPEqktwu", + "outputId": "9ee2b97d-adeb-4e98-ba08-96610652e37c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(4424, 36)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# plot out the numeric columns to view the distribution\n", + "numericColumns = data.loc[:, data.select_dtypes(include=['number']).columns]\n", + "numericColumns.shape" + ] + }, + { + "cell_type": "markdown", + "id": "sTAPYPWSIaWu", + "metadata": { + "id": "sTAPYPWSIaWu" + }, + "source": [ + "There are 36 numercial columns to plot. Let's size the grid accordingly (12 x 3) and plot each column showing the mean and median line." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "UFu58n1VlbBB", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "UFu58n1VlbBB", + "outputId": "2fd0f6e7-2230-4fd4-a25a-ee64fd6fcedb" + }, + "outputs": [ + { + "data": { + "image/png": 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nzxqSjB9++ME877333jMkGe+++26Ksdwrsf39kouhRIkShqurq3H27FnztJs3bxre3t5G3759zdO2bNliSDK2bNlinjZgwIBk1/PJJ58YkoyrV68+VLwpSWn5hmEYlStXNtq3b5/uz99v3759hiRj5cqVKbbZuXOnIcmYNGmSxfTz588bbm5uxogRI8zTEs+1ffv2maf9888/Ru7cuQ03Nzfjjz/+ME8PCwszJBnTpk1LNcbdu3cbkoxly5al2i7xXNq2bZshyTh48KB5Xo8ePQxJxtSpUy0+89FHHxmSjO3bt5unSTLc3NyMqKgo87Tbt28bFSpUMMqWLWueltJ37ujRo4Yko3///slux5tvvmmeVr16daNOnToW7WbNmmVIMsLDww3DMIxz584ZefLkMQYNGmTR7t9//zX8/f2NTp06GYZhGHfu3DGKFCliVK9e3UhISDC3i4iIMJycnIwSJUok2WcbNmwwJBnr1q1LMg8A4LjISZMiJ02KnNRSSjnp4cOHDUnGyJEjLaYvXbrUkPTQ34/73b5924iLizPKlStnDBkyxDx95MiRhslkMsLCwizaN2nSxOIciYmJMby9vY02bdpYtLtz545RpUoV44knnkiyzi5duhh+fn4PjA0A8GDkW0mRbyVFvmUppXwrJibGcHZ2Nj744APDMAzjwoUL5vzLzc3NuHXrlmEYhtGnTx+jSJEi5s8ld/5+/PHHKZ6nD3uuPP/884aLi4tx7tw5i8+3aNHCcHd3N58XKX0nkjvXWrVqley1u+Q87LVCwzCMZ555xnjkkUeMO3fumKetXbvWkGSsXr062eWn9l1N6Tt279+oRPf/zbnf7du3jfj4eKN3795GtWrVLOZ5eHgku8z7913i9dDAwECLbfz3338NX19fi+uuiX83Jk6caLHM/v37G66urubrqWn5fs+bN8+QZBw9evSBbZE1MZQ64ACeffbZZKcXKFBADRs2fODn161bJ1dX10ztJXDx4kX169dPxYoVU548eeTk5KQSJUpIko4ePZqkfUrblFFVq1ZV8eLFze9dXV0VEBCgs2fPpmt5jz/+uCSpU6dO+uabb/THH39YJc57PfHEE1q3bp3eeOMNbd26VTdv3szQ8sqWLasCBQpo5MiRmjNnjo4cOZKkzY8//iiTyaSuXbvq9u3b5pe/v7+qVKmSZOiawoULq0aNGub33t7e8vX1VdWqVS3uCq1YseL/sXfncVHV+x/H38O+iAgYIIqG5a5ZaZlLoaGYS6berpX7kkuYiUuWebtRGaRely6mqZmYpNbtamWLiRtl5EZZaaZZ5gqihbghoJzfH/6Y6wgIIswM8Ho+Hufx8Hy/3zPzOWcWP5zPnO+RpCKP9/HjxyVJ/v7++fp+//139e3bV4GBgXJ0dJSzs7NCQ0MlFfxe6tevn8V63r21N23aZNEeFhZm8QtYR0dHPfbYYzpw4IDF9FJS/vdn3mNdO4XQvffeq0aNGllMXTlkyBAlJSVp37595rYlS5bonnvuUdOmTSVJX375pS5duqSBAwdaHH83NzeFhoaaj/++fft0/Phx9e3b1+IXinXq1Cn0V955x7Qs3qsAgMqNnLT4yEnLd06amJgo6crxvtqjjz4qJ6eCJ+Yr6L106dIlRUdHq3HjxnJxcZGTk5NcXFz066+/WrwfN23apCZNmqh58+YW2+fltXmSkpL0119/adCgQRbHKzc3Vw899JB27NiR70o8f39/paWl6dKlS9c7FAAAO0G+VXzkW+U73/Lw8FDr1q21fv16SVem/a5WrZqeffZZZWdna8uWLZKuXEVe2NXixVWc98rGjRsVFhaWbzaIwYMH68KFC/mm8C5txT1XKF0593j06FHzsZOunHsMDAxUly5dzG03+lktqf/85z9q27atqlSpYn6exYsXl/g58s6HDhgwwGJ2jCpVquhvf/ubtm7dmu82mlffwkKS7rjjDl28eFFpaWmSbuzzzbnV8o/COFDGqlevLg8PDx08eLDMnqOw6XIKa7/WyZMnFRQUVOz73dyo3NxchYeHa9WqVZo0aZI2bNig7du3a+vWrZJUYJJX3NhvlJ+fX742V1fXEieaDzzwgD766CNzYlKrVi01bdrUfO+f0vDvf/9bzz33nD766CN16NBBvr6+6tmzp3799dcSPZ63t7cSExN155136oUXXlCTJk0UFBSkl156yXx/6xMnTsgwDAUEBMjZ2dli2bp1a757zfj6+uZ7HhcXl3ztLi4ukq7cX+Z68l4PNzc3i/Zz587p/vvv17Zt2zR16lRt3rxZO3bs0KpVqyy2y+Pk5JTvNQ8MDJQk8zRU17YXZ+y178+8/oLet0FBQRbb9+vXT66uruZ7Hf3888/asWOHhgwZYh5z4sQJSVeSsmuP//vvv28+/nmPe73Yr5V3TG/2jysAQPlCTkpOerPISf8nLwe7+keVUsG5Z56C3kvjx4/Xiy++qJ49e2rNmjXatm2bduzYoebNm1u8F/78889i5Xt5OeSjjz6a73hNmzZNhmHor7/+stjGzc1NhmEUeSwAAEUj3yLfulnkW5Y6duyorVu36vz581q/fr0efPBB+fn5qUWLFlq/fr0OHjyogwcP3nRhvDjvlT///LPQ8355/WWpuOcKJalLly6qUaOG+d7v6enp+uSTTzRw4EA5OjpKKtlntSRWrVqlPn36qGbNmoqPj9e3336rHTt2aOjQoSXOP4s6D5ubm6v09HSL9mtfY1dXV0n/288b+XxzbrX84x7jQBlzdHRUWFiYvvjiCx09elS1atUqchtXV1dlZWXlay/sP9jC7mVR3Htc3HLLLdqyZYtyc3MLTYzzvvCvjas4/+nv3r1bP/zwg+Li4jRo0CBz+4EDBwrdprixXx1X3n9okvIlbWXpkUce0SOPPKKsrCxt3bpVMTEx6tu3r2699VbzPXBuhqenp15++WW9/PLLOnHihPmXow8//LB++eWXEj1ms2bNtHLlShmGoR9//FFxcXF65ZVX5O7urueff17Vq1eXyWTS119/bXFc8xTUVpry7nVz7Ym7jRs36vjx49q8ebP5KnHpyj26C3Lp0iX9+eefFslPamqqpPwJUV57QW3Xjr32/ZnXn5KSku8zfvz4cfP+SFd+xf3II4/o3Xff1dSpU7VkyRK5ubnpiSeeMI/JG//hhx+af6lZkLznvV7s18o7plfHBACo+MhJyUlvFjnp/+TlYCdOnFDNmjXN7Xm5Z0EKei/l3Q88Ojraov3UqVOqVq2axfMVJ9/Lizc2Nlb33XdfgXFcW8z/66+/5OrqqipVqhQ4HgBQfORb5Fs3i3zLUlhYmF588UV99dVX2rBhg1566SVz+7p16xQSEmJeL2t+fn5KSUnJ1553xXvefhT2+bnZ92lxzxVKV76LBgwYoH//+986ffq0li9frqysLIuLckryWb2am5tbgd9dp06dsjjnGB8fr5CQEL3//vsWn/WCti2uq8/DXuv48eNycHCQj4/PDT9ucT/fnFst/7hiHLCCyZMnyzAMDR8+XNnZ2fn6c3JytGbNGvP6rbfeqh9//NFizMaNG3Xu3Lkyia9Lly66ePGi+QrWggQEBMjNzS1fXB9//HGRj5/3n961idSCBQtuPNhr3HrrrZKUL66rj2dpuPZXZIWNCQ0N1bRp0yRJ33//fanGIF15HQYPHqwnnnhC+/btM08LU5z4CmIymdS8eXPNnj1b1apV03fffSdJ6t69uwzD0LFjx9SyZct8S7NmzUp3x66RN93Sb7/9li9e6cbeS++9957F+vLlyyVJ7du3t2jfsGGD+deXknT58mW9//77uu2224r8YzZvurL4+HiL9h07dmjv3r35EvQhQ4bo+PHj+vzzzxUfH69evXpZnPzs3LmznJyc9NtvvxV4/Fu2bClJatCggWrUqKEVK1bIMAzz9ocOHVJSUlKBsf7++++SpMaNG193nwAAFQ85KTlpaansOekDDzwgSXr//fct2j/88MMbmpLcZDLlez9+9tln+aZl7NChg/bs2aMffvjBoj0vr83Ttm1bVatWTT///HOhOWTe1Vt5fv/9d/JCAChF5FvkW6Wlsudb0pWp5atWrao5c+YoNTVVnTp1knTlSvLvv/9eH3zwgRo3bmwxhXtBSnrMrhYWFma+YOdq7777rjw8PMw/SizsffrJJ58UGFdxYyruucI8Q4YM0cWLF7VixQrFxcWpdevWatiwobn/Zj+rBX137d+/3+LWkXnP4+LiYlEUT01NLfD7pLjHo0GDBqpZs6aWL19ucT70/Pnz+u9//6vWrVvLw8OjWPtRkKI+37///rscHBzUoEGDEj8HbIsrxgEraN26tebPn6+IiAi1aNFCTz31lJo0aaKcnBx9//33WrhwoZo2baqHH35YkjRgwAC9+OKL+uc//6nQ0FD9/PPPmjt3rry9vcskvieeeEJLlizRqFGjtG/fPnXo0EG5ubnatm2bGjVqpMcff9x8r5l33nlHt912m5o3b67t27fnOxlTkIYNG+q2227T888/L8Mw5OvrqzVr1ighIeGmY+/atat8fX01bNgwvfLKK3JyclJcXJyOHDly0499tbwkcNq0aerSpYscHR11xx13aOrUqTp69KjCwsJUq1YtnT59Wm+88YbFfa8Lc+jQIe3YsUPS/5K/Dz/8UNKV5CIvoWnVqpW6d++uO+64Qz4+Ptq7d6+WLVtm8Z98YfFde+JLunLvoHnz5qlnz56qW7euDMPQqlWrdPr0aXOC2bZtW40YMUJDhgzRzp079cADD8jT01MpKSnasmWLmjVrpqeeeupmD2uhatWqpbp162rr1q165plnzO1t2rSRj4+PRo0apZdeeknOzs5677338p0kzOPi4qKZM2fq3Llzuueee5SUlKSpU6eqS5cuateuncXY6tWr68EHH9SLL74oT09PzZs3T7/88otWrlxZZLwNGjTQiBEjFBsbKwcHB3Xp0kV//PGHXnzxRQUHB2v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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot layout of numeric columns\n", + "plt.subplots(12, 3, figsize=(20, 40)) # using a 4 x 7 grid of graphs since we are working with 28 columns; each graph will be 20x20 in size\n", + "plt.suptitle(\"Numeric Column Distributions\", size=20)\n", + "\n", + "for i , column in enumerate(numericColumns.columns, 1):\n", + " plt.subplot(12, 3 , i)\n", + " sns.histplot(numericColumns[column])\n", + "\n", + " plt.xticks(rotation=90)\n", + "\n", + " # titles and labels\n", + " plt.title(column)\n", + " plt.xlabel(column)\n", + " plt.ylabel(\"Frequency\")\n", + "\n", + " # include median and mean lines\n", + " plt.axvline(data[column].mean(), c='blue', label='Mean')\n", + " plt.axvline(data[column].median(), c='green', label='Median')\n", + " plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cFJyLCcxoahU", + "metadata": { + "id": "cFJyLCcxoahU" + }, + "source": [ + "This helps in giving a picture of the distribution of all the numeric columns:\n", + "\n", + "- Marital Status is skewed towards Single.\n", + "- Nationality is skewed towards Portuguese.\n", + "\n", + "Somewhat normally distributed features are:\n", + "\n", + "- Previous qualification (grade)\n", + "- Admission grade\n", + "- Circular units 1st & 2nd sem (grade) (but with predominance on zero's)\n", + "\n", + "Binary features:\n", + "\n", + "- Daytime/evening attendance (majority in Daytime)\n", + "- Displaced (close to balanced but Displaced is dominant)\n", + "- Educational special needs (mostly no)\n", + "Debtor (mostly no)\n", + "- Tuition fees up to date (majority are)\n", + "- Gender (female are close to twice as much as male)\n", + "- Scholarship holder (majority no)\n", + "- International (very small fraction are)\n", + "\n", + "Unemployment rate, Inflation rate, GDP are probably specific to the student's country. According to the `.nunique` results earlier there seems to be 10 different countries\n", + "\n", + "Age of enrollment seems to have a natural progression where the majority age of students are 18-20 and it tapers down as the age increases\n", + "\n", + "There are a few numerical variables that are categorical and have high cardinality which needs to be addressed to minimize the features.\n", + "\n", + "- Application mode\n", + "- Application order\n", + "- Course\n", + "- Fathers occupation\n", + "- Mothers occupation\n", + "- Nationality\n", + "- Marital status\n", + "- Previous qualification\n", + "- Fathers qualification\n", + "- Mothers qualification\n", + "\n", + "I will attempt to use binning or some other encoding where possible on these later.\n", + "\n", + "Going back to see if Unemployment rate, Infation rate, and GDP are all specific to the country of where the student resides, let's look at the combination of each." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ZXlijS96Hhhp", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZXlijS96Hhhp", + "outputId": "094d229f-bdd2-4beb-d303-e10136a43850" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Unique Rows: 10\n", + " Unemployment rate Inflation rate GDP\n", + "0 10.8 1.4 1.74\n", + "1 13.9 -0.3 0.79\n", + "3 9.4 -0.8 -3.12\n", + "5 16.2 0.3 -0.92\n", + "6 15.5 2.8 -4.06\n", + "9 8.9 1.4 3.51\n", + "11 12.7 3.7 -1.70\n", + "20 11.1 0.6 2.02\n", + "25 7.6 2.6 0.32\n", + "33 12.4 0.5 1.79\n" + ] + } + ], + "source": [ + "unique_Country_Combinations = data[['Unemployment rate', 'Inflation rate', 'GDP']].drop_duplicates().sort_index()\n", + "print('Total Unique Rows:', unique_Country_Combinations.shape[0])\n", + "print(unique_Country_Combinations)" + ] + }, + { + "cell_type": "markdown", + "id": "5uw-ZC0UITTU", + "metadata": { + "id": "5uw-ZC0UITTU" + }, + "source": [ + "So the 3 columns Unemployment rate, Infation rate, and GDP are all related to the same country. With a total of 10 of them." + ] + }, + { + "cell_type": "markdown", + "id": "wtT9YjcWIY3r", + "metadata": { + "id": "wtT9YjcWIY3r" + }, + "source": [ + "# Target Variable\n", + "What's the distribution of our target variable like. Since the target variable is the only string (or object type), we'll use .describe and only include object type." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "dMYwVhMKIrWV", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 174 + }, + "id": "dMYwVhMKIrWV", + "outputId": "1d2d7de2-159f-4cb5-c69b-549d1f033d26" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Target
count4424
unique3
topGraduate
freq2209
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" + ], + "text/plain": [ + " Target\n", + "count 4424\n", + "unique 3\n", + "top Graduate\n", + "freq 2209" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe(include='object')" + ] + }, + { + "cell_type": "markdown", + "id": "m7swFc6OJBri", + "metadata": { + "id": "m7swFc6OJBri" + }, + "source": [ + "Our target has no nulls and has 3 unique values. The most frequent is Graduate and that has 2209 instances in the data. Now let's plot the distribution of the 3 values." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "IvJ9RcSbqDf8", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "IvJ9RcSbqDf8", + "outputId": "73ba82d5-3f6b-4319-db53-6fd36b1c72a4" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x='Target', data=data)\n", + "plt.title('Distribution of Target Classes')\n", + "\n", + "# show percentage of each Target value on plot\n", + "total = len(data)\n", + "for p in plt.gca().patches:\n", + " height = p.get_height()\n", + " plt.text(p.get_x() + p.get_width() / 2.,\n", + " height + 0.1,\n", + " '{:.1f}%'.format((height / total) * 100),\n", + " ha=\"center\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "780dRmR_uCLW", + "metadata": { + "id": "780dRmR_uCLW" + }, + "source": [ + "As described on the [website](https://archive.ics.uci.edu/dataset/697/predict+students+dropout+and+academic+success) source of this data, there are 3 classes on the categorical Target variable. The Graduate value is the most at close to 50% of the data. Dropout at 32% is 2nd and then Enrolled is place with 17%.\n", + "\n", + "Essentially for modelling this needs to be a numerical column, label encoded. Let's do that now and use the ID maps below:\n", + "\n", + "- Dropout - 1 - 32.1%\n", + "- Graduate - 2 - 49.9%\n", + "- Enrolled - 3 - 17.9%\n", + "\n", + "We'll update Dropout to 1, Graduate to 2 and Enrolled to 3.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "sSijI7iYN6dw", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sSijI7iYN6dw", + "outputId": "6288729d-8600-4063-bce4-44759f0c7f5c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample rows of Target variable: 3377 2\n", + "1297 1\n", + "3467 2\n", + "1444 2\n", + "4036 2\n", + "826 1\n", + "2124 2\n", + "1891 2\n", + "4012 1\n", + "1928 3\n", + "Name: TargetEncoded, dtype: int64\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "class_mapping = {'Graduate': 2, 'Enrolled': 3, 'Dropout': 1}\n", + "\n", + "data['TargetEncoded'] = data['Target'].map(class_mapping)\n", + "print('Sample rows of Target variable: ', data['TargetEncoded'].sample(10))" + ] + }, + { + "cell_type": "markdown", + "id": "ztq8bl74O4Mw", + "metadata": { + "id": "ztq8bl74O4Mw" + }, + "source": [ + "The sample of the Target variable above confirms that it has been remapped to numeric values.\n", + "\n", + "Plot the new target column just to check." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "VrS9ZfzmPV0b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "VrS9ZfzmPV0b", + "outputId": "f9f32607-b9dc-451e-f5a1-708815603b99" + }, + "outputs": [ + { + "data": { + "image/png": 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dOXPG6NGjh3HPPfcYNpvNaZvK5QmnnTt3Gp07dzbsdrvh6upq1KlTx5g7d65TzeWnzL788kun9stPhV1Zn5t169YZjzzyiOHp6Wl4eHgYjRs3Nr7++utc+yuIp8z27NljtGnTxvD29jZKly5t9OzZ0zhy5EiOz+BqP7NLly4Zo0aNMvz8/Ax3d3ejcePGxsaNGw273W48//zzTrUnT540Ro4caVSsWNEoUaKE4ePjY9SrV8+YMGGCcf78ebMur2PnahITE41x48YZNWrUMEqWLGm4ubkZlStXNoYMGWLs3Lkzx7783YYNG4yQkBCjZMmSRtmyZY2BAwcav/76q9PP7fjx40b//v2N6tWrG56enoaXl5dRu3ZtY+rUqUZmZqZhGNf/u5GRkWF88MEHRp06dQx3d3fDy8vLqF69ujFkyBAjLi7uhvoCrmQzjGs8GgMAuC02bNigf/zjH1qwYIH69OlT2MMBLIVABACFICoqShs3blS9evXk4eGh3377Te+8847sdrt27Nhh3uwO4PbgHiIAKASlSpXSmjVrNG3aNJ07d05lypRRaGioJk2aRBgCCgFniAAAgOXx2D0AALA8AhEAALA8AhEAALA8bqq+TtnZ2frzzz/l7e1d4C96AwAAt4ZhGDp37pwCAwOdXnp6JQLRdfrzzz9z/NVuAABwZzh69KjKly+f53IC0XW6/IcDjx49qlKlShXyaAAAwPVISUlRUFCQ0x8Azg2B6DpdvkxWqlQpAhEAAHeYa93uwk3VAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAO5okyZNks1mU3h4uNl2/Phx9e/fX4GBgSpZsqTat2+vuLi4q/aTkZGhN954Q5UqVZK7u7vq1KmjyMhIp5oFCxYoKChIPj4+euGFF5yWHTp0SFWrVlVKSkqB7RuA24dABOCOtWXLFv373/9W7dq1zTbDMPToo4/q4MGDWrlypbZv367g4GC1bt1aqampefb18ssva/bs2Zo+fbr27NmjoUOHqlu3btq+fbsk6dSpUxo4cKA++OADfffdd5o3b55Wr15trv/MM8/onXfe4bUcwB2KQATgjnT+/Hk9+eST+uSTT1S6dGmzPS4uTps2bdKsWbPUoEEDVatWTTNnztT58+e1aNGiPPv7/PPP9dJLL6lDhw6677779Mwzz6hdu3aaPHmyJOngwYOy2+3q1auXGjRooJYtW2rPnj2SpIULF8rV1VXdu3e/tTsN4JYhEAG4Iw0fPlwdO3ZU69atndrT0tIkSe7u7mabi4uLXF1dtX79+jz7S0tLc1pHkjw8PMx1qlSpogsXLmj79u06c+aMtmzZotq1a+vMmTN69dVXNWPGjILaNQCFgEAE4I6zePFi/frrr5o0aVKOZdWrV1dwcLDGjx+vpKQkpaen65133lFiYqISEhLy7LNdu3aaMmWK4uLilJ2draioKK1cudJcp3Tp0po3b57CwsLUsGFDhYWFqV27dhozZoxGjBih+Ph4Pfjgg6pZs6aWLl16y/YdwK3Bn+4AcEc5evSonnvuOa1ZsybHGR1JKlGihJYtW6YBAwbIx8dHLi4uat26tUJDQ6/a74cffqhBgwapevXqstlsqlSpkp566inNnTvXrOnWrZu6detmzkdHR2vnzp2aMWOGKleurEWLFsnhcKhhw4Zq1qyZ/Pz8Cm7HAdxSnCECcEfZtm2bTpw4oXr16ql48eIqXry4YmJi9NFHH6l48eLKyspSvXr1FBsbq7NnzyohIUGRkZE6ffq0KlasmGe/ZcuW1VdffaXU1FQdPnxY+/btk5eXV57rpKWladiwYZo9e7YOHDigzMxMNW/eXNWqVVPVqlW1efPmW/URALgFCEQA7iitWrXSzp07FRsba07169fXk08+qdjYWLm4uJi1drtdZcuWVVxcnLZu3aquXbtes393d3eVK1dOmZmZWrZsWZ7r/POf/1RoaKgeeughZWVlKTMz01yWkZGhrKysm99ZALcNl8wA3FG8vb1Vs2ZNpzZPT0/5+vqa7V9++aXKli2rChUqaOfOnXruuef06KOPqm3btuY6YWFhKleunHkf0ubNm3Xs2DHVrVtXx44d08SJE5Wdna2xY8fmGMPu3bu1ZMkSxcbGSvrrvqVixYppzpw5cjgc2rdvnxo0aHCLPgEAtwKBCMBdJyEhQaNGjdLx48cVEBCgsLAwvfLKK041R44cUbFi/ztJfunSJb388ss6ePCgvLy81KFDB33++ee65557nNYzDEODBw/W1KlT5enpKemvp9EiIiI0fPhwpaWlacaMGSpXrtwt308ABcdmGIZR2IO4E6SkpMhutys5OZkXr+GuUu+Fzwp7CChCtr0fVthDAArU9X5/cw8RAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwPAIRAACwvEINRJMmTVKDBg3k7e0tPz8/Pfroo9q/f79TjWEYmjhxogIDA+Xh4aEWLVpo9+7dTjVpaWkaMWKEypQpI09PT3Xp0kV//PGHU01SUpL69u0ru90uu92uvn376uzZs7d6FwEAwB2gUANRTEyMhg8frk2bNikqKkqZmZlq27atUlNTzZr33ntPU6ZM0YwZM7RlyxY5HA61adNG586dM2vCw8O1YsUKLV68WOvXr9f58+fVqVMnZWVlmTV9+vRRbGysIiMjFRkZqdjYWPXt2/e27i8AACiabIZhGIU9iMtOnjwpPz8/xcTEqFmzZjIMQ4GBgQoPD9e4ceMk/XU2yN/fX++++66GDBmi5ORklS1bVp9//rl69eolSfrzzz8VFBSkb7/9Vu3atdPevXv1wAMPaNOmTWrUqJEkadOmTQoJCdG+fftUrVq1a44tJSVFdrtdycnJKlWq1K37EIDbrN4LnxX2EFCEbHs/rLCHABSo6/3+LlL3ECUnJ0uSfHx8JEnx8fFKTExU27ZtzRo3Nzc1b95cGzZskCRt27ZNGRkZTjWBgYGqWbOmWbNx40bZ7XYzDElS48aNZbfbzZorpaWlKSUlxWkCAAB3pyITiAzD0KhRo/Twww+rZs2akqTExERJkr+/v1Otv7+/uSwxMVGurq4qXbr0VWv8/PxybNPPz8+sudKkSZPM+43sdruCgoJubgcBAECRVWQC0bPPPqsdO3Zo0aJFOZbZbDanecMwcrRd6cqa3Oqv1s/48eOVnJxsTkePHr2e3QAAAHegIhGIRowYoVWrVmnt2rUqX7682e5wOCQpx1mcEydOmGeNHA6H0tPTlZSUdNWa48eP59juyZMnc5x9uszNzU2lSpVymgAAwN2pUAORYRh69tlntXz5cv3444+qWLGi0/KKFSvK4XAoKirKbEtPT1dMTIyaNGkiSapXr55KlCjhVJOQkKBdu3aZNSEhIUpOTtYvv/xi1mzevFnJyclmDQAAsK7ihbnx4cOHa+HChVq5cqW8vb3NM0F2u10eHh6y2WwKDw/X22+/rSpVqqhKlSp6++23VbJkSfXp08esHTBggEaPHi1fX1/5+PhozJgxqlWrllq3bi1Juv/++9W+fXsNGjRIs2fPliQNHjxYnTp1uq4nzAAAwN2tUAPRrFmzJEktWrRwap87d6769+8vSRo7dqwuXryoYcOGKSkpSY0aNdKaNWvk7e1t1k+dOlXFixfX448/rosXL6pVq1aKiIiQi4uLWbNgwQKNHDnSfBqtS5cumjFjxq3dQQAAcEcoUu8hKsp4DxHuVryHCH/He4hwt7kj30MEAABQGAhEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8ghEAADA8go1EP3000/q3LmzAgMDZbPZ9NVXXzkt79+/v2w2m9PUuHFjp5q0tDSNGDFCZcqUkaenp7p06aI//vjDqSYpKUl9+/aV3W6X3W5X3759dfbs2Vu8dwAA4E5RqIEoNTVVderU0YwZM/Ksad++vRISEszp22+/dVoeHh6uFStWaPHixVq/fr3Onz+vTp06KSsry6zp06ePYmNjFRkZqcjISMXGxqpv3763bL8AAMCdpXhhbjw0NFShoaFXrXFzc5PD4ch1WXJysubMmaPPP/9crVu3liTNnz9fQUFB+v7779WuXTvt3btXkZGR2rRpkxo1aiRJ+uSTTxQSEqL9+/erWrVqBbtTAADgjlPk7yGKjo6Wn5+fqlatqkGDBunEiRPmsm3btikjI0Nt27Y12wIDA1WzZk1t2LBBkrRx40bZ7XYzDElS48aNZbfbzRoAAGBthXqG6FpCQ0PVs2dPBQcHKz4+Xq+88ooeeeQRbdu2TW5ubkpMTJSrq6tKly7ttJ6/v78SExMlSYmJifLz88vRt5+fn1mTm7S0NKWlpZnzKSkpBbRXAACgqCnSgahXr17mv2vWrKn69esrODhYq1evVvfu3fNczzAM2Ww2c/7v/86r5kqTJk3S66+/ns+RAwCAO0mRv2T2dwEBAQoODlZcXJwkyeFwKD09XUlJSU51J06ckL+/v1lz/PjxHH2dPHnSrMnN+PHjlZycbE5Hjx4twD0BAABFyR0ViE6fPq2jR48qICBAklSvXj2VKFFCUVFRZk1CQoJ27dqlJk2aSJJCQkKUnJysX375xazZvHmzkpOTzZrcuLm5qVSpUk6TFc2aNUu1a9c2P4OQkBD93//9nyQpIyND48aNU61ateTp6anAwECFhYXpzz//vGqfu3fv1mOPPaZ7771XNptN06ZNy1GzYMECBQUFycfHRy+88ILTskOHDqlq1apcxgQAFJhCvWR2/vx5HThwwJyPj49XbGysfHx85OPjo4kTJ+qxxx5TQECADh06pJdeekllypRRt27dJEl2u10DBgzQ6NGj5evrKx8fH40ZM0a1atUynzq7//771b59ew0aNEizZ8+WJA0ePFidOnXiCbPrUL58eb3zzjuqXLmyJGnevHnq2rWrtm/frvLly+vXX3/VK6+8ojp16igpKUnh4eHq0qWLtm7dmmefFy5c0H333aeePXvq+eefz7H81KlTGjhwoCIiInTfffepY8eOatGihTp27ChJeuaZZ/TOO+9YNqQCAApeoQairVu3qmXLlub8qFGjJEn9+vXTrFmztHPnTn322Wc6e/asAgIC1LJlSy1ZskTe3t7mOlOnTlXx4sX1+OOP6+LFi2rVqpUiIiLk4uJi1ixYsEAjR440n0br0qXLVd99hP/p3Lmz0/xbb72lWbNmadOmTRowYIDT2TlJmj59uho2bKgjR46oQoUKufbZoEEDNWjQQJL04osv5lh+8OBB2e128x6yli1bas+ePerYsaMWLlwoV1fXq95DBgDAjSrUQNSiRQsZhpHn8u++++6afbi7u2v69OmaPn16njU+Pj6aP39+vsaI/8nKytKXX36p1NRUhYSE5FqTnJwsm82me+65J9/bqVKlii5cuKDt27crODhYW7Zs0dNPP60zZ87o1Vdf1dq1a/PdNwAAubmj7iFC4di5c6e8vLzk5uamoUOHasWKFXrggQdy1F26dEkvvvii+vTpc1OXs0qXLq158+YpLCxMDRs2VFhYmNq1a6cxY8ZoxIgRio+P14MPPqiaNWtq6dKlN7NrAABIKuKP3aNoqFatmmJjY3X27FktW7ZM/fr1U0xMjFMoysjIUO/evZWdna2ZM2fe9Da7detm3ism/fWCzp07d2rGjBmqXLmyFi1aJIfDoYYNG6pZs2a5vmsKAIDrxRkiXJOrq6sqV66s+vXra9KkSapTp44+/PBDc3lGRoYef/xxxcfHKyoqqsBvdk5LS9OwYcM0e/ZsHThwQJmZmWrevLmqVaumqlWravPmzQW6PQCA9RCIcMMMwzDf4n05DMXFxen777+Xr69vgW/vn//8p0JDQ/XQQw8pKytLmZmZ5rKMjAynP+QLAEB+cMkMV/XSSy8pNDRUQUFBOnfunBYvXqzo6GhFRkYqMzNTPXr00K+//qpvvvlGWVlZ5p9D8fHxkaurqyQpLCxM5cqV06RJkyRJ6enp2rNnj/nvY8eOKTY2Vl5eXubj/Zft3r1bS5YsUWxsrCSpevXqKlasmObMmSOHw6F9+/aZT6wBAJBfBCJc1fHjx9W3b18lJCTIbrerdu3aioyMVJs2bXTo0CGtWrVKklS3bl2n9dauXasWLVpIko4cOaJixf53MvLPP//Ugw8+aM5/8MEH+uCDD9S8eXNFR0eb7YZhaPDgwZo6dao8PT0lSR4eHoqIiNDw4cOVlpamGTNmqFy5crdm5wEAlmEzrvbcO0wpKSmy2+1KTk7O9z0y9V74rIBHhTvdtvfDCnsIHJdwUhSOSaAgXe/3N/cQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAy8tXIHrkkUd09uzZHO0pKSl65JFHbnZMAAAAt1W+AlF0dLTS09NztF+6dEnr1q276UEBAADcTsVvpHjHjh3mv/fs2aPExERzPisrS5GRkSpXrlzBjQ4AAOA2uKFAVLduXdlsNtlstlwvjXl4eGj69OkFNjgAAIDb4YYCUXx8vAzD0H333adffvlFZcuWNZe5urrKz89PLi4uBT5IAACAW+mGAlFwcLAkKTs7+5YMBgAAoDDcUCD6u99//13R0dE6ceJEjoD06quv3vTAAAAAbpd8BaJPPvlEzzzzjMqUKSOHwyGbzWYus9lsBCIAAHBHyVcgevPNN/XWW29p3LhxBT0eAACA2y5f7yFKSkpSz549C3osAAAAhSJfgahnz55as2ZNQY8FAACgUOTrklnlypX1yiuvaNOmTapVq5ZKlCjhtHzkyJEFMjgAAIDbIV+B6N///re8vLwUExOjmJgYp2U2m41ABAAA7ij5CkTx8fEFPQ4AAIBCk697iAAAAO4m+TpD9PTTT191+aeffpqvwQAAABSGfAWipKQkp/mMjAzt2rVLZ8+ezfWPvgIAABRl+QpEK1asyNGWnZ2tYcOG6b777rvpQQEAANxOBXYPUbFixfT8889r6tSpBdUlAADAbVGgN1X/97//VWZmZkF2CQAAcMvl65LZqFGjnOYNw1BCQoJWr16tfv36FcjAAAAAbpd8BaLt27c7zRcrVkxly5bV5MmTr/kEGgAAQFGTr0C0du3agh4HAABAoclXILrs5MmT2r9/v2w2m6pWraqyZcsW1LgAAABum3zdVJ2amqqnn35aAQEBatasmZo2barAwEANGDBAFy5cKOgxAgAA3FL5CkSjRo1STEyMvv76a509e1Znz57VypUrFRMTo9GjRxf0GAEAAG6pfF0yW7ZsmZYuXaoWLVqYbR06dJCHh4cef/xxzZo1q6DGBwAAcMvl6wzRhQsX5O/vn6Pdz8+PS2YAAOCOk69AFBISotdee02XLl0y2y5evKjXX39dISEhBTY4AACA2yFfl8ymTZum0NBQlS9fXnXq1JHNZlNsbKzc3Ny0Zs2agh4jAADALZWvQFSrVi3FxcVp/vz52rdvnwzDUO/evfXkk0/Kw8OjoMcIAABwS+UrEE2aNEn+/v4aNGiQU/unn36qkydPaty4cQUyOAAAgNshX/cQzZ49W9WrV8/RXqNGDX388cc3PSgAAIDbKV+BKDExUQEBATnay5Ytq4SEhJseFAAAwO2Ur0AUFBSkn3/+OUf7zz//rMDAwJseFAAAwO2Ur3uIBg4cqPDwcGVkZOiRRx6RJP3www8aO3Ysb6oGAAB3nHwForFjx+rMmTMaNmyY0tPTJUnu7u4aN26cxo8fX6ADBAAAuNXyFYhsNpveffddvfLKK9q7d688PDxUpUoVubm5FfT4AAAAbrl83UN0mZeXlxo0aKCaNWvmKwz99NNP6ty5swIDA2Wz2fTVV185LTcMQxMnTlRgYKA8PDzUokUL7d6926kmLS1NI0aMUJkyZeTp6akuXbrojz/+cKpJSkpS3759ZbfbZbfb1bdvX509e/aGxwsAAO5ONxWIblZqaqrq1KmjGTNm5Lr8vffe05QpUzRjxgxt2bJFDodDbdq00blz58ya8PBwrVixQosXL9b69et1/vx5derUSVlZWWZNnz59FBsbq8jISEVGRio2NlZ9+/a95fsHAADuDPm6ZFZQQkNDFRoamusywzA0bdo0TZgwQd27d5ckzZs3T/7+/lq4cKGGDBmi5ORkzZkzR59//rlat24tSZo/f76CgoL0/fffq127dtq7d68iIyO1adMmNWrUSJL0ySefKCQkRPv371e1atVuz84CAIAiq1DPEF1NfHy8EhMT1bZtW7PNzc1NzZs314YNGyRJ27ZtU0ZGhlNNYGCgatasadZs3LhRdrvdDEOS1LhxY9ntdrMmN2lpaUpJSXGaAADA3anIBqLExERJkr+/v1O7v7+/uSwxMVGurq4qXbr0VWv8/Pxy9O/n52fW5GbSpEnmPUd2u11BQUE3tT8AAKDoKrKB6DKbzeY0bxhGjrYrXVmTW/21+hk/frySk5PN6ejRozc4cgAAcKcosoHI4XBIUo6zOCdOnDDPGjkcDqWnpyspKemqNcePH8/R/8mTJ3Ocffo7Nzc3lSpVymkCAAB3pyIbiCpWrCiHw6GoqCizLT09XTExMWrSpIkkqV69eipRooRTTUJCgnbt2mXWhISEKDk5Wb/88otZs3nzZiUnJ5s1AADA2gr1KbPz58/rwIED5nx8fLxiY2Pl4+OjChUqKDw8XG+//baqVKmiKlWq6O2331bJkiXVp08fSZLdbteAAQM0evRo+fr6ysfHR2PGjFGtWrXMp87uv/9+tW/fXoMGDdLs2bMlSYMHD1anTp14wgwAAEgq5EC0detWtWzZ0pwfNWqUJKlfv36KiIjQ2LFjdfHiRQ0bNkxJSUlq1KiR1qxZI29vb3OdqVOnqnjx4nr88cd18eJFtWrVShEREXJxcTFrFixYoJEjR5pPo3Xp0iXPdx8BAADrsRmGYRT2IO4EKSkpstvtSk5Ozvf9RPVe+KyAR4U73bb3wwp7CByXcFIUjkmgIF3v93eRvYcIAADgdiEQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQAAyyMQAQBQAH766Sd17txZgYGBstls+uqrr5yW22y2XKf3338/zz4zMjL0xhtvqFKlSnJ3d1edOnUUGRnpVLNgwQIFBQXJx8dHL7zwgtOyQ4cOqWrVqkpJSSmw/bxbEYgAACgAqampqlOnjmbMmJHr8oSEBKfp008/lc1m02OPPZZnny+//LJmz56t6dOna8+ePRo6dKi6deum7du3S5JOnTqlgQMH6oMPPtB3332nefPmafXq1eb6zzzzjN555x2VKlWqYHf2LlS8sAcAAMDdIDQ0VKGhoXkudzgcTvMrV65Uy5Ytdd999+W5zueff64JEyaoQ4cOkv4KON99950mT56s+fPn6+DBg7Lb7erVq5ckqWXLltqzZ486duyohQsXytXVVd27dy+Avbv7cYYIAIDb7Pjx41q9erUGDBhw1bq0tDS5u7s7tXl4eGj9+vWSpCpVqujChQvavn27zpw5oy1btqh27do6c+aMXn311TzPViEnAhEAALfZvHnz5O3tfc2zN+3atdOUKVMUFxen7OxsRUVFaeXKlUpISJAklS5dWvPmzVNYWJgaNmyosLAwtWvXTmPGjNGIESMUHx+vBx98UDVr1tTSpUtvx67dsbhkBgDAbfbpp5/qySefzHH250offvihBg0apOrVq8tms6lSpUp66qmnNHfuXLOmW7du6tatmzkfHR2tnTt3asaMGapcubIWLVokh8Ohhg0bqlmzZvLz87tl+3Un4wwRAAC30bp167R//34NHDjwmrVly5bVV199pdTUVB0+fFj79u2Tl5eXKlasmGt9Wlqahg0bptmzZ+vAgQPKzMxU8+bNVa1aNVWtWlWbN28u6N25axCIAAC4jebMmaN69eqpTp06172Ou7u7ypUrp8zMTC1btkxdu3bNte6f//ynQkND9dBDDykrK0uZmZnmsoyMDGVlZd30+O9WXDIDAKAAnD9/XgcOHDDn4+PjFRsbKx8fH1WoUEGSlJKSoi+//FKTJ0/OtY+wsDCVK1dOkyZNkiRt3rxZx44dU926dXXs2DFNnDhR2dnZGjt2bI51d+/erSVLlig2NlaSVL16dRUrVkxz5syRw+HQvn371KBBgwLe67sHgQgAgAKwdetWtWzZ0pwfNWqUJKlfv36KiIiQJC1evFiGYeiJJ57ItY8jR46oWLH/Xby5dOmSXn75ZR08eFBeXl7q0KGDPv/8c91zzz1O6xmGocGDB2vq1Kny9PSU9NfTaBERERo+fLjS0tI0Y8YMlStXrgD3+O5iMwzDKOxB3AlSUlJkt9uVnJyc7xdc1XvhswIeFe50294PK+whcFzCCcckipqbPSav9/ube4gAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlEYgAAIDlFelANHHiRNlsNqfJ4XCYyw3D0MSJExUYGCgPDw+1aNFCu3fvduojLS1NI0aMUJkyZeTp6akuXbrojz/+uN27AgAAirAiHYgkqUaNGkpISDCnnTt3msvee+89TZkyRTNmzNCWLVvkcDjUpk0bnTt3zqwJDw/XihUrtHjxYq1fv17nz59Xp06dlJWVVRi7AwAAiqDihT2AaylevLjTWaHLDMPQtGnTNGHCBHXv3l2SNG/ePPn7+2vhwoUaMmSIkpOTNWfOHH3++edq3bq1JGn+/PkKCgrS999/r3bt2t3WfQEAAEVTkT9DFBcXp8DAQFWsWFG9e/fWwYMHJUnx8fFKTExU27ZtzVo3Nzc1b95cGzZskCRt27ZNGRkZTjWBgYGqWbOmWZOXtLQ0paSkOE0AAODuVKQDUaNGjfTZZ5/pu+++0yeffKLExEQ1adJEp0+fVmJioiTJ39/faR1/f39zWWJiolxdXVW6dOk8a/IyadIk2e12cwoKCirAPQMAAEVJkQ5EoaGheuyxx1SrVi21bt1aq1evlvTXpbHLbDab0zqGYeRou9L11IwfP17JycnmdPTo0XzuBQAAKOqKdCC6kqenp2rVqqW4uDjzvqIrz/ScOHHCPGvkcDiUnp6upKSkPGvy4ubmplKlSjlNAADg7nRHBaK0tDTt3btXAQEBqlixohwOh6Kioszl6enpiomJUZMmTSRJ9erVU4kSJZxqEhIStGvXLrMGAACgSD9lNmbMGHXu3FkVKlTQiRMn9OabbyolJUX9+vWTzWZTeHi43n77bVWpUkVVqlTR22+/rZIlS6pPnz6SJLvdrgEDBmj06NHy9fWVj4+PxowZY16CAwAAkIp4IPrjjz/0xBNP6NSpUypbtqwaN26sTZs2KTg4WJI0duxYXbx4UcOGDVNSUpIaNWqkNWvWyNvb2+xj6tSpKl68uB5//HFdvHhRrVq1UkREhFxcXAprtwAAQBFTpAPR4sWLr7rcZrNp4sSJmjhxYp417u7umj59uqZPn17AowMAAHeLO+oeIgAAgFuBQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACyPQAQAACzPUoFo5syZqlixotzd3VWvXj2tW7eusIcEAACKAMsEoiVLlig8PFwTJkzQ9u3b1bRpU4WGhurIkSOFPTQAAFDILBOIpkyZogEDBmjgwIG6//77NW3aNAUFBWnWrFmFPTQAAFDILBGI0tPTtW3bNrVt29apvW3bttqwYUMhjQoAABQVxQt7ALfDqVOnlJWVJX9/f6d2f39/JSYm5rpOWlqa0tLSzPnk5GRJUkpKSr7HkZV2Md/r4u50M8dTQeG4xN9xTKKoudlj8vL6hmFctc4Sgegym83mNG8YRo62yyZNmqTXX389R3tQUNAtGRusyT59aGEPAXDCMYmipqCOyXPnzslut+e53BKBqEyZMnJxcclxNujEiRM5zhpdNn78eI0aNcqcz87O1pkzZ+Tr65tniMK1paSkKCgoSEePHlWpUqUKeziAJI5LFD0ckwXHMAydO3dOgYGBV62zRCBydXVVvXr1FBUVpW7dupntUVFR6tq1a67ruLm5yc3NzantnnvuuZXDtJRSpUrxS44ih+MSRQ3HZMG42pmhyywRiCRp1KhR6tu3r+rXr6+QkBD9+9//1pEjRzR0KKeHAQCwOssEol69eun06dN64403lJCQoJo1a+rbb79VcHBwYQ8NAAAUMssEIkkaNmyYhg0bVtjDsDQ3Nze99tprOS5HAoWJ4xJFDcfk7WczrvUcGgAAwF3OEi9mBAAAuBoCEQAAsDwCEQAAsDwCEQAAsDwCEW6bn376SZ07d1ZgYKBsNpu++uqrwh4SLGzSpElq0KCBvL295efnp0cffVT79+8v7GHB4mbNmqXatWubL2QMCQnR//3f/xX2sCyBQITbJjU1VXXq1NGMGTMKeyiAYmJiNHz4cG3atElRUVHKzMxU27ZtlZqaWthDg4WVL19e77zzjrZu3aqtW7fqkUceUdeuXbV79+7CHtpdj8fuUShsNptWrFihRx99tLCHAkiSTp48KT8/P8XExKhZs2aFPRzA5OPjo/fff18DBgwo7KHc1Sz1YkYAyEtycrKkv758gKIgKytLX375pVJTUxUSElLYw7nrEYgAWJ5hGBo1apQefvhh1axZs7CHA4vbuXOnQkJCdOnSJXl5eWnFihV64IEHCntYdz0CEQDLe/bZZ7Vjxw6tX7++sIcCqFq1aoqNjdXZs2e1bNky9evXTzExMYSiW4xABMDSRowYoVWrVumnn35S+fLlC3s4gFxdXVW5cmVJUv369bVlyxZ9+OGHmj17diGP7O5GIAJgSYZhaMSIEVqxYoWio6NVsWLFwh4SkCvDMJSWllbYw7jrEYhw25w/f14HDhww5+Pj4xUbGysfHx9VqFChEEcGKxo+fLgWLlyolStXytvbW4mJiZIku90uDw+PQh4drOqll15SaGiogoKCdO7cOS1evFjR0dGKjIws7KHd9XjsHrdNdHS0WrZsmaO9X79+ioiIuP0DgqXZbLZc2+fOnav+/fvf3sEA/78BAwbohx9+UEJCgux2u2rXrq1x48apTZs2hT20ux6BCAAAWB5vqgYAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAIAAJZHIAKAQtaiRQuFh4ffVB8RERG65557CmQ8gBURiADki81mu+pUmG97vvfeezVt2jSntujo6DzHevnPdgCwLv6WGYB8SUhIMP+9ZMkSvfrqq9q/f7/ZdqN/Dyw9PV2urq4FNr687N+/X6VKlXJq8/Pzu+XbBVC0cYYIQL44HA5zstvtstls5nyJEiU0dOhQlS9fXiVLllStWrW0aNEip/VbtGihZ599VqNGjVKZMmXMv9W0atUqValSRR4eHmrZsqXmzZsnm82ms2fPmutu2LBBzZo1k4eHh4KCgjRy5Eilpqaa/R4+fFjPP/+8eQbo7/z8/JzG7nA4VKzYX/8p7N+/vx599FF98MEHCggIkK+vr4YPH66MjAxz/bS0NI0dO1ZBQUFyc3NTlSpVNGfOHHN5TEyMGjZsKDc3NwUEBOjFF19UZmamuTw1NVVhYWHy8vJSQECAJk+enOOzTU9P19ixY1WuXDl5enqqUaNGio6OdqqJiIhQhQoVVLJkSXXr1k2nT5++gZ8egCsRiAAUuEuXLqlevXr65ptvtGvXLg0ePFh9+/bV5s2bnermzZun4sWL6+eff9bs2bN16NAh9ejRQ48++qhiY2M1ZMgQTZgwwWmdnTt3ql27durevbt27NihJUuWaP369Xr22WclScuXL1f58uX1xhtvKCEhwelM1vVYu3at/vvf/2rt2rWaN2+eIiIinP74cFhYmBYvXqyPPvpIe/fu1ccffywvLy9J0rFjx9ShQwc1aNBAv/32m2bNmqU5c+bozTffNNd/4YUXtHbtWq1YsUJr1qxRdHS0tm3b5jSGp556Sj///LMWL16sHTt2qGfPnmrfvr3i4uIkSZs3b9bTTz+tYcOGKTY2Vi1btnTaBoB8MADgJs2dO9ew2+1XrenQoYMxevRoc7558+ZG3bp1nWrGjRtn1KxZ06ltwoQJhiQjKSnJMAzD6Nu3rzF48GCnmnXr1hnFihUzLl68aBiGYQQHBxtTp051qlm7dq0hyfD09HSaqlatatb069fPCA4ONjIzM822nj17Gr169TIMwzD2799vSDKioqJy3ceXXnrJqFatmpGdnW22/etf/zK8vLyMrKws49y5c4arq6uxePFic/np06cNDw8P47nnnjMMwzAOHDhg2Gw249ixY059t2rVyhg/frxhGIbxxBNPGO3bt3da3qtXr2v+DADkjXuIABS4rKwsvfPOO1qyZImOHTumtLQ0paWlydPT06mufv36TvP79+9XgwYNnNoaNmzoNL9t2zYdOHBACxYsMNsMw1B2drbi4+N1//33X3Vs69atk7e3tzlfvLjzfwZr1KghFxcXcz4gIEA7d+6UJMXGxsrFxUXNmzfPte+9e/cqJCTE6TLdP/7xD50/f15//PGHkpKSlJ6erpCQEHO5j4+PqlWrZs7/+uuvMgxDVatWdeo7LS1Nvr6+5na6devmtDwkJESRkZFX3XcAeSMQAShwkydP1tSpUzVt2jTVqlVLnp6eCg8PV3p6ulPdlQHJMIwc9/wYhuE0n52drSFDhmjkyJE5tluhQoVrjq1ixYpXfTy9RIkSTvM2m03Z2dmSrn2j+NXGb7PZcuxLbrKzs+Xi4qJt27Y5BTNJ5qW56+kHwI0hEAEocOvWrVPXrl31//7f/5P015d8XFzcNc/eVK9eXd9++61T29atW53mH3roIe3evVuVK1fOsx9XV1dlZWXlc/R5q1WrlrKzsxUTE6PWrVvnWP7AAw9o2bJlTsFow4YN8vb2Vrly5VS6dGmVKFFCmzZtMsNbUlKSfv/9d/Os04MPPqisrCydOHFCTZs2zXUcDzzwgDZt2uTUduU8gBvDTdUAClzlypUVFRWlDRs2aO/evRoyZMh1vetnyJAh2rdvn8aNG6fff/9dX3zxhXlD8+WAMW7cOG3cuFHDhw9XbGys4uLitGrVKo0YMcLs595779VPP/2kY8eO6dSpU07bOHHihBITE52mvz9FdjX33nuv+vXrp6efflpfffWV4uPjFR0drS+++EKSNGzYMB09elQjRozQvn37tHLlSr322msaNWqUihUrJi8vLw0YMEAvvPCCfvjhB+3atUv9+/c3n3KTpKpVq+rJJ59UWFiYli9frvj4eG3ZskXvvvuuGRZHjhypyMhIvffee/r99981Y8YMLpcBN4lABKDAvfLKK3rooYfUrl07tWjRQg6HQ48++ug116tYsaKWLl2q5cuXq3bt2po1a5b5lJmbm5skqXbt2oqJiVFcXJyaNm2qBx98UK+88ooCAgLMft544w0dOnRIlSpVUtmyZZ22Ua1aNQUEBDhNVz7ldTWzZs1Sjx49NGzYMFWvXl2DBg0yH/kvV66cvv32W/3yyy+qU6eOhg4dqgEDBujll18213///ffVrFkzdenSRa1bt9bDDz+sevXqOW1j7ty5CgsL0+jRo1WtWjV16dJFmzdvVlBQkCSpcePG+s9//qPp06erbt26WrNmjdM2ANw4m8HFaABF2FtvvaWPP/5YR48eLeyhALiLcQ8RgCJl5syZatCggXx9ffXzzz/r/fffN98xBAC3CoEIQJESFxenN998U2fOnFGFChU0evRojR8/vrCHBeAuxyUzAABgedxUDQAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALI9ABAAALO//A/LFDcC6zIT/AAAAAElFTkSuQmCC", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x='TargetEncoded', data=data)\n", + "plt.title('Distribution of Target Classes')\n", + "\n", + "# show percentage of each Target value on plot\n", + "total = len(data)\n", + "for p in plt.gca().patches:\n", + " height = p.get_height()\n", + " plt.text(p.get_x() + p.get_width() / 2.,\n", + " height + 0.1,\n", + " '{:.1f}%'.format((height / total) * 100),\n", + " ha=\"center\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "NJm6NyG2Pect", + "metadata": { + "id": "NJm6NyG2Pect" + }, + "source": [ + "Looks good and identical to the original Target columns. So we will now drop the original column." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "Ql8DZq7FPgTQ", + "metadata": { + "id": "Ql8DZq7FPgTQ" + }, + "outputs": [], + "source": [ + "data.drop(columns=['Target'], inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "EbPvrBEtPxtw", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EbPvrBEtPxtw", + "outputId": "fc56b901-c038-4952-f4c6-f4c94380020f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Marital status Application mode Application order Course Daytime/evening attendance\\t Previous qualification \\\n", + "4134 1 1 1 9238 1 1 \n", + "\n", + " Previous qualification (grade) Nationality Mothers qualification Fathers qualification Mothers occupation Fathers occupation \\\n", + "4134 142.0 1 34 38 4 7 \n", + "\n", + " Admission grade Displaced Educational special needs Debtor Tuition fees up to date Gender Scholarship holder Age at enrollment \\\n", + "4134 127.3 0 0 0 1 0 1 18 \n", + "\n", + " International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) Curricular units 1st sem (evaluations) \\\n", + "4134 0 0 6 12 \n", + "\n", + " Curricular units 1st sem (approved) Curricular units 1st sem (grade) Curricular units 1st sem (without evaluations) \\\n", + "4134 4 11.0 0 \n", + "\n", + " Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) Curricular units 2nd sem (evaluations) \\\n", + "4134 0 6 9 \n", + "\n", + " Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) Curricular units 2nd sem (without evaluations) Unemployment rate \\\n", + "4134 6 11.5 0 9.4 \n", + "\n", + " Inflation rate GDP TargetEncoded \n", + "4134 -0.8 -3.12 2 \n" + ] + } + ], + "source": [ + "# Check that the original Target columns is removed\n", + "print(data.sample())" + ] + }, + { + "cell_type": "markdown", + "id": "SxKLtlZSP3ha", + "metadata": { + "id": "SxKLtlZSP3ha" + }, + "source": [ + "I will also take the next step and convert this 3 class Target variable into binary since Graduate and Enrolled can be seen as the same in terms of a student dropping out or not. Both are considered Not Dropped. So we will combine Graduate (2) and Enrolled (3) into one bucket. To make it simple I will update both Graduate (2) and Enrolled (3) to 0. This will convert the TargetEncoded variable to binary where having the 0 value represents Not Dropped while 1 means Dropped." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "JVi0IDTJP9B6", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 643 + }, + "id": "JVi0IDTJP9B6", + "outputId": "c1fb20e8-7fad-4a2c-a1dd-193063b7f58a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Count Breakdown of Target:\n", + " TargetEncoded\n", + "0 3003\n", + "1 1421\n", + "Name: count, dtype: int64\n", + "\n", + "Percentage Breakdown of Target:\n", + " TargetEncoded\n", + "0 67.879747\n", + "1 32.120253\n", + "Name: proportion, dtype: float64\n" + ] + }, + { + "data": { + "text/html": [ + "
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Marital statusApplication modeApplication orderCourseDaytime/evening attendance\\tPrevious qualificationPrevious qualification (grade)NationalityMothers qualificationFathers qualificationMothers occupationFathers occupationAdmission gradeDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentInternationalCurricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Unemployment rateInflation rateGDPTargetEncoded
0117517111122.01191259127.310011020000000.000000000000.000000010.81.41.741
11151925411160.011333142.5100010190066614.0000000066613.666667013.9-0.30.790
2115907011122.01373799124.810001019006000.000000006000.000000010.81.41.741
31172977311122.01383753119.6100100200068613.42857100610512.40000009.4-0.8-3.120
42391801401100.01373899141.5000100450069512.3333330066613.000000013.9-0.30.790
523919991019133.11373797114.80011105000510511.85714300517511.500000516.20.3-0.920
6111950011142.011938710128.4100101180079713.3000000088814.345000015.52.8-4.060
71184925411119.01373799113.110001022005500.000000005500.000000015.52.8-4.061
8113923811137.0621199129.3000101211068613.8750000067614.142857016.20.3-0.920
9111923811138.0111947123.0101000180069511.40000000614213.50000008.91.43.511
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" + ], + "text/plain": [ + " Marital status Application mode Application order Course Daytime/evening attendance\\t Previous qualification \\\n", + "0 1 17 5 171 1 1 \n", + "1 1 15 1 9254 1 1 \n", + "2 1 1 5 9070 1 1 \n", + "3 1 17 2 9773 1 1 \n", + "4 2 39 1 8014 0 1 \n", + "5 2 39 1 9991 0 19 \n", + "6 1 1 1 9500 1 1 \n", + "7 1 18 4 9254 1 1 \n", + "8 1 1 3 9238 1 1 \n", + "9 1 1 1 9238 1 1 \n", + "\n", + " Previous qualification (grade) Nationality Mothers qualification Fathers qualification Mothers occupation Fathers occupation \\\n", + "0 122.0 1 19 12 5 9 \n", + "1 160.0 1 1 3 3 3 \n", + "2 122.0 1 37 37 9 9 \n", + "3 122.0 1 38 37 5 3 \n", + "4 100.0 1 37 38 9 9 \n", + "5 133.1 1 37 37 9 7 \n", + "6 142.0 1 19 38 7 10 \n", + "7 119.0 1 37 37 9 9 \n", + "8 137.0 62 1 1 9 9 \n", + "9 138.0 1 1 19 4 7 \n", + "\n", + " Admission grade Displaced Educational special needs Debtor Tuition fees up to date Gender Scholarship holder Age at enrollment \\\n", + "0 127.3 1 0 0 1 1 0 20 \n", + "1 142.5 1 0 0 0 1 0 19 \n", + "2 124.8 1 0 0 0 1 0 19 \n", + "3 119.6 1 0 0 1 0 0 20 \n", + "4 141.5 0 0 0 1 0 0 45 \n", + "5 114.8 0 0 1 1 1 0 50 \n", + "6 128.4 1 0 0 1 0 1 18 \n", + "7 113.1 1 0 0 0 1 0 22 \n", + "8 129.3 0 0 0 1 0 1 21 \n", + "9 123.0 1 0 1 0 0 0 18 \n", + "\n", + " International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) Curricular units 1st sem (evaluations) \\\n", + "0 0 0 0 0 \n", + "1 0 0 6 6 \n", + "2 0 0 6 0 \n", + "3 0 0 6 8 \n", + "4 0 0 6 9 \n", + "5 0 0 5 10 \n", + "6 0 0 7 9 \n", + "7 0 0 5 5 \n", + "8 1 0 6 8 \n", + "9 0 0 6 9 \n", + "\n", + " Curricular units 1st sem (approved) Curricular units 1st sem (grade) Curricular units 1st sem (without evaluations) \\\n", + "0 0 0.000000 0 \n", + "1 6 14.000000 0 \n", + "2 0 0.000000 0 \n", + "3 6 13.428571 0 \n", + "4 5 12.333333 0 \n", + "5 5 11.857143 0 \n", + "6 7 13.300000 0 \n", + "7 0 0.000000 0 \n", + "8 6 13.875000 0 \n", + "9 5 11.400000 0 \n", + "\n", + " Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) Curricular units 2nd sem (evaluations) \\\n", + "0 0 0 0 \n", + "1 0 6 6 \n", + "2 0 6 0 \n", + "3 0 6 10 \n", + "4 0 6 6 \n", + "5 0 5 17 \n", + "6 0 8 8 \n", + "7 0 5 5 \n", + "8 0 6 7 \n", + "9 0 6 14 \n", + "\n", + " Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) Curricular units 2nd sem (without evaluations) Unemployment rate \\\n", + "0 0 0.000000 0 10.8 \n", + "1 6 13.666667 0 13.9 \n", + "2 0 0.000000 0 10.8 \n", + "3 5 12.400000 0 9.4 \n", + "4 6 13.000000 0 13.9 \n", + "5 5 11.500000 5 16.2 \n", + "6 8 14.345000 0 15.5 \n", + "7 0 0.000000 0 15.5 \n", + "8 6 14.142857 0 16.2 \n", + "9 2 13.500000 0 8.9 \n", + "\n", + " Inflation rate GDP TargetEncoded \n", + "0 1.4 1.74 1 \n", + "1 -0.3 0.79 0 \n", + "2 1.4 1.74 1 \n", + "3 -0.8 -3.12 0 \n", + "4 -0.3 0.79 0 \n", + "5 0.3 -0.92 0 \n", + "6 2.8 -4.06 0 \n", + "7 2.8 -4.06 1 \n", + "8 0.3 -0.92 0 \n", + "9 1.4 3.51 1 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Update values 2, 3 to 0\n", + "data.loc[data['TargetEncoded'].isin([2, 3]), 'TargetEncoded'] = 0\n", + "# check and print count of all unique values\n", + "print('\\nCount Breakdown of Target:\\n', data['TargetEncoded'].value_counts())\n", + "print('\\nPercentage Breakdown of Target:\\n', data['TargetEncoded'].value_counts(normalize=True) * 100)\n", + "data.head(10)" + ] + }, + { + "cell_type": "markdown", + "id": "nIKmVNGGQH49", + "metadata": { + "id": "nIKmVNGGQH49" + }, + "source": [ + "The above data now shows 2 values (0 and 1) for the Target variable making this a binary classification.\n", + "\n", + "Let's plot a graph for better visualtization." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "_NNvLUNKQLsN", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "_NNvLUNKQLsN", + "outputId": "8a37122e-a002-41fc-c09f-fb05f38f7a80" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.lines import Line2D\n", + "\n", + "sns.countplot(x='TargetEncoded', data=data)\n", + "plt.title('Distribution of Updated Target Classes')\n", + "\n", + "# show percentage of each Target value on plot\n", + "total = len(data)\n", + "for p in plt.gca().patches:\n", + " height = p.get_height()\n", + " plt.text(p.get_x() + p.get_width() / 2.,\n", + " height + 0.1,\n", + " '{:.1f}%'.format((height / total) * 100),\n", + " ha=\"center\")\n", + " # Add custom legend labels\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ebzbN9ynQgd5", + "metadata": { + "id": "ebzbN9ynQgd5" + }, + "source": [ + "The target variable has been binarized.\n", + "\n", + "Target variable Count and Percentage breakdown:\n", + "\n", + "| TargetEncoded | Count | Percentage |\n", + "|---------------|-------|------------|\n", + "| 0 - Not Dropped | 3003 | 67.88% |\n", + "| 1 - Dropped | 1421 | 32.12% |\n", + "\n", + "\n", + "The ratio between Not Dropped and Dropped is about 70 : 30 indicating an imbalance. Imbalanced datasets can pose challenges for machine learning modeling, since some algorithms assume balanced class distribution or have difficulty learning from minority class samples. This can be remedied by certain techniques such as resampling." + ] + }, + { + "cell_type": "markdown", + "id": "EDTkmREkQ5pp", + "metadata": { + "id": "EDTkmREkQ5pp" + }, + "source": [ + "# Target-Variable-Count-Plot-Across-Features\n", + "\n", + "Now let's look at the Count plot of the TargetEncoded variable across all the other features. The graph on the right are the drop out targets. It will not be sharing the y axis values to better see how the variable differs with each target value whether it's the dropped out population or not. Granted some of the variables will not make sense with this plot such as those showing grades or credits." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "beqhz0h0RKEs", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "beqhz0h0RKEs", + "outputId": "35995d95-8776-4595-964b-da39705bbf14" + }, + "outputs": [], + "source": [ + "i = 1\n", + "for col in data.drop(columns=['TargetEncoded']):\n", + " g = sns.catplot(x = col, kind='count', col = 'TargetEncoded', data=data, sharey=True)\n", + " g.figure.suptitle(i)\n", + " g.set_xticklabels()\n", + " i += 1" + ] + }, + { + "cell_type": "markdown", + "id": "84KhHAUxRjfA", + "metadata": { + "id": "84KhHAUxRjfA" + }, + "source": [ + "From plotting the Target against each feature we can observe the following:\n", + "\n", + "- In Application mode there's a stark rise under the Dropped population for value: 39 - Over 23 years old. \n", + "\n", + "- It's hard to see the exact value due to the high unique values but for Course, the far right bar plot in pink (9991 - Management evening attendance) shows a much higher count for Dropped.\n", + "\n", + "- The Displaced comparison with the Target seems surprising. There's a lot more Displaced that have not dropped out than dropped.\n", + "\n", + "- Debtor shows a higher count for those that are dropped.\n", + "\n", + "- Same for those with Tuition fees that are NOT up to date. It's higher for the dropped.\n", + "\n", + "- For Gender, the amount of dropped between male and female pretty balanced. There are much more female enrolled though compared to males, more than twice the amount.\n" + ] + }, + { + "cell_type": "markdown", + "id": "8-Cix4SURypR", + "metadata": { + "id": "8-Cix4SURypR" + }, + "source": [ + "## High Percentage of Dropped across Variable Values\n", + "Another look to possibly see some patterns with the Dropped Target is to calculate what percentage of a category value are Dropped vs Not Dropped. Plot only the ones that have a higher percentage when Dropped. Let's give a threshold of when Dropped percentage is above 65. Let's loop through the columns and only show the values of the features that have a high Dropped percentage compared to Not Dropped." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "23aA1xnGR5jv", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "23aA1xnGR5jv", + "outputId": "45ee4ef8-5b08-496f-83d5-9d37493bb949" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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nU9HR0RVYCQAA1wfqJwAA3lNlA3xSUpL+9Kc/KSEhQQ6Ho0z3Nca43ae4+1/e5nJTp06Vy+WythMnTpRpDgAAXI+onwAAeE+VDfCff/65MjIy1KhRI/n6+srX11fHjh3ThAkT1KRJE0lSZGSk8vLylJmZ6XbfjIwMRUREWG1+/PHHIv2fOnXKalOcgIAA1a5d220DAABXRv0EAMB7qmyAHzZsmPbt26fk5GRri4qK0qRJk/Tpp59KkmJiYuTn56fExETrfmlpaTpw4IC6du0qSYqNjZXL5dLOnTutNl9++aVcLpfVBgAAAACAqq5SP4U+Oztb33zzjXU7NTVVycnJCg0NVaNGjRQWFubW3s/PT5GRkdYnxzudTsXFxWnChAkKCwtTaGioJk6cqPbt21ufSt+6dWv17dtXjzzyiF599VVJ0qOPPqp+/frxCfQAAAAAANuo1AC/e/du3Xnnndbt8ePHS5JGjBihhISEUvWxcOFC+fr6avDgwcrJyVGPHj2UkJAgHx8fq83bb7+tcePGWZ9W379//6t+9zwAAAAAAFWJwxhjKnsSdpCVlSWn0ymXy8X7+QAAKCXqJwAAnlNl3wMPAAAAAAD+gwAPAAAAAIANEOABAAAAALABAjwAAAAAADZQqZ9Cb1cxk970aH9J84Z7tD8AAAAAQPXDFXgAAAAAAGyAAA8AAAAAgA0Q4AEAAAAAsAECPAAAAAAANkCABwAAAADABgjwAAAAAADYAAEeAAAAAAAbIMADAAAAAGADBHgAAAAAAGyAAA8AAAAAgA0Q4AEAAAAAsAECPAAAAAAANkCABwAAAADABgjwAAAAAADYAAEeAAAAAAAbIMADAAAAAGADBHgAAAAAAGyAAA8AAAAAgA0Q4AEAAAAAsAECPAAAAAAANkCABwAAAADABgjwAAAAAADYAAEeAAAAAAAbIMADAAAAAGADBHgAAAAAAGyAAA8AAAAAgA0Q4AEAAAAAsAECPAAAAAAANkCABwAAAADABgjwAAAAAADYAAEeAAAAAAAbIMADAAAAAGADBHgAAAAAAGyAAA8AAAAAgA0Q4AEAAAAAsAECPAAAAAAANkCABwAAAADABgjwAAAAAADYAAEeAAAAAAAbIMADAAAAAGADBHgAAAAAAGygUgP8li1bdO+99yoqKkoOh0Nr1qyxjuXn52vKlClq3769goODFRUVpeHDh+uHH35w6yM3N1djx45VvXr1FBwcrP79++vkyZNubTIzMzVs2DA5nU45nU4NGzZMp0+fvgYrBAAAAADAMyo1wJ89e1YdOnTQkiVLihw7d+6c9uzZo+nTp2vPnj364IMPdPjwYfXv39+tXXx8vFavXq1Vq1Zp69atys7OVr9+/VRQUGC1GTp0qJKTk7Vu3TqtW7dOycnJGjZsmNfXBwAAAACApziMMaayJyFJDodDq1ev1sCBA0tss2vXLt1yyy06duyYGjVqJJfLpfr16+utt97SkCFDJEk//PCDoqOj9cknn6hPnz5KSUlRmzZttGPHDnXp0kWStGPHDsXGxurrr79Wy5YtSzW/rKwsOZ1OuVwu3fn8moou103SvOEe7Q8AgKri0vpZu3btyp4OAAC2Zqv3wLtcLjkcDtWpU0eSlJSUpPz8fPXu3dtqExUVpXbt2mnbtm2SpO3bt8vpdFrhXZJuvfVWOZ1Oq01xcnNzlZWV5bYBAIAro34CAOA9tgnwv/zyi5566ikNHTrU+gt+enq6/P39VbduXbe2ERERSk9Pt9qEh4cX6S88PNxqU5zZs2db75l3Op2Kjo724GoAAKieqJ8AAHiPLQJ8fn6+HnjgARUWFuqVV165antjjBwOh3X70n+X1OZyU6dOlcvlsrYTJ06Ub/IAAFxHqJ8AAHiPb2VP4Gry8/M1ePBgpaam6p///Kfb++ciIyOVl5enzMxMt6vwGRkZ6tq1q9Xmxx9/LNLvqVOnFBERUeK4AQEBCggI8OBKAACo/qifAAB4T5W+An8xvB85ckQbNmxQWFiY2/GYmBj5+fkpMTHR2peWlqYDBw5YAT42NlYul0s7d+602nz55ZdyuVxWGwAAAAAAqrpKvQKfnZ2tb775xrqdmpqq5ORkhYaGKioqSr/97W+1Z88e/f3vf1dBQYH1nvXQ0FD5+/vL6XQqLi5OEyZMUFhYmEJDQzVx4kS1b99ePXv2lCS1bt1affv21SOPPKJXX31VkvToo4+qX79+pf4EegAAAAAAKlulBvjdu3frzjvvtG6PHz9ekjRixAjNnDlTa9eulSTdfPPNbvfbuHGjunfvLklauHChfH19NXjwYOXk5KhHjx5KSEiQj4+P1f7tt9/WuHHjrE+r79+/f7HfPQ8AAAAAQFVVZb4Hvqrje+ABACg7vgceAADPqdLvgQcAAAAAABcQ4AEAAAAAsAECPAAAAAAANkCABwAAAADABgjwAAAAAADYAAEeAAAAAAAbIMADAAAAAGADBHgAAAAAAGyAAA8AAAAAgA0Q4AEAAAAAsAECPAAAAAAANkCABwAAAADABgjwAAAAAADYAAEeAAAAAAAbIMADAAAAAGADBHgAAAAAAGyAAA8AAAAAgA0Q4AEAAAAAsAECPAAAAAAANkCABwAAAADABgjwAAAAAADYAAEeAAAAAAAbIMADAAAAAGADBHgAAAAAAGyAAA8AAAAAgA0Q4AEAAAAAsAECPAAAAAAANkCABwAAAADABgjwAAAAAADYAAEeAAAAAAAbIMADAAAAAGADBHgAAAAAAGyAAA8AAAAAgA0Q4AEAAAAAsAECPAAAAAAANkCABwAAAADABgjwAAAAAADYAAEeAAAAAAAbIMADAAAAAGADBHgAAAAAAGyAAA8AAAAAgA0Q4AEAAAAAsAECPAAAAAAANkCABwAAAADABgjwAAAAAADYAAEeAAAAAAAbqNQAv2XLFt17772KioqSw+HQmjVr3I4bYzRz5kxFRUUpMDBQ3bt318GDB93a5ObmauzYsapXr56Cg4PVv39/nTx50q1NZmamhg0bJqfTKafTqWHDhun06dNeXh0AAAAAAJ5TqQH+7Nmz6tChg5YsWVLs8blz52rBggVasmSJdu3apcjISPXq1Utnzpyx2sTHx2v16tVatWqVtm7dquzsbPXr108FBQVWm6FDhyo5OVnr1q3TunXrlJycrGHDhnl9fQAAAAAAeIrDGGMqexKS5HA4tHr1ag0cOFDShavvUVFRio+P15QpUyRduNoeERGhOXPmaOTIkXK5XKpfv77eeustDRkyRJL0ww8/KDo6Wp988on69OmjlJQUtWnTRjt27FCXLl0kSTt27FBsbKy+/vprtWzZslTzy8rKktPplMvl0p3Pr/Ho2pPmDfdofwAAVBWX1s/atWtX9nQAALC1Kvse+NTUVKWnp6t3797WvoCAAHXr1k3btm2TJCUlJSk/P9+tTVRUlNq1a2e12b59u5xOpxXeJenWW2+V0+m02hQnNzdXWVlZbhsAALgy6icAAN5TZQN8enq6JCkiIsJtf0REhHUsPT1d/v7+qlu37hXbhIeHF+k/PDzcalOc2bNnW++Zdzqdio6OrtB6AAC4HlA/AQDwniob4C9yOBxut40xRfZd7vI2xbW/Wj9Tp06Vy+WythMnTpRx5gAAXH+onwAAeI9vZU+gJJGRkZIuXEFv0KCBtT8jI8O6Kh8ZGam8vDxlZma6XYXPyMhQ165drTY//vhjkf5PnTpV5Or+pQICAhQQEOCRtQAAcL2gfgIA4D1V9gp806ZNFRkZqcTERGtfXl6eNm/ebIXzmJgY+fn5ubVJS0vTgQMHrDaxsbFyuVzauXOn1ebLL7+Uy+Wy2gAAAAAAUNVV6hX47OxsffPNN9bt1NRUJScnKzQ0VI0aNVJ8fLxmzZql5s2bq3nz5po1a5aCgoI0dOhQSZLT6VRcXJwmTJigsLAwhYaGauLEiWrfvr169uwpSWrdurX69u2rRx55RK+++qok6dFHH1W/fv1K/Qn0AAAAAABUtkoN8Lt379add95p3R4/frwkacSIEUpISNDkyZOVk5OjUaNGKTMzU126dNH69esVEhJi3WfhwoXy9fXV4MGDlZOTox49eighIUE+Pj5Wm7ffflvjxo2zPq2+f//+JX73PAAAAAAAVVGV+R74qo7vgQcAoOz4HngAADynyr4HHgAAAAAA/AcBHgAAAAAAGyDAAwAAAABgAwR4AAAAAABsgAAPAAAAAIANEOABAAAAALABAjwAAAAAADZAgAcAAAAAwAYI8AAAAAAA2AABHgAAAAAAGyDAAwAAAABgAwR4AAAAAABsgAAPAAAAAIANEOABAAAAALABAjwAAAAAADZAgAcAAAAAwAYI8AAAAAAA2AABHgAAAAAAGyDAAwAAAABgAwR4AAAAAABsgAAPAAAAAIANEOABAAAAALABAjwAAAAAADZAgAcAAAAAwAYI8AAAAAAA2AABHgAAAAAAGyDAAwAAAABgAwR4AAAAAABsgAAPAAAAAIANEOABAAAAALABAjwAAAAAADZAgAcAAAAAwAYI8AAAAAAA2AABHgAAAAAAGyDAAwAAAABgAwR4AAAAAABsgAAPAAAAAIANEOABAAAAALABAjwAAAAAADZAgAcAAAAAwAYI8AAAAAAA2AABHgAAAAAAGyDAAwAAAABgAwR4AAAAAABsgAAPAAAAAIANVOkAf/78eT399NNq2rSpAgMDdeONN+q5555TYWGh1cYYo5kzZyoqKkqBgYHq3r27Dh486NZPbm6uxo4dq3r16ik4OFj9+/fXyZMnr/VyAAAAAAAotyod4OfMmaO//vWvWrJkiVJSUjR37lzNmzdPixcvttrMnTtXCxYs0JIlS7Rr1y5FRkaqV69eOnPmjNUmPj5eq1ev1qpVq7R161ZlZ2erX79+KigoqIxlAQAAAABQZr6VPYEr2b59uwYMGKB77rlHktSkSRO9++672r17t6QLV98XLVqkadOm6f7775ckvfHGG4qIiNA777yjkSNHyuVyafny5XrrrbfUs2dPSdLKlSsVHR2tDRs2qE+fPpWzOAAAAAAAyqBKX4G//fbb9dlnn+nw4cOSpK+++kpbt27Vb37zG0lSamqq0tPT1bt3b+s+AQEB6tatm7Zt2yZJSkpKUn5+vlubqKgotWvXzmoDAAAAAEBVV6WvwE+ZMkUul0utWrWSj4+PCgoK9OKLL+p3v/udJCk9PV2SFBER4Xa/iIgIHTt2zGrj7++vunXrFmlz8f7Fyc3NVW5urnU7KyvLI2sCAKA6o34CAOA9VfoK/HvvvaeVK1fqnXfe0Z49e/TGG2/o5Zdf1htvvOHWzuFwuN02xhTZd7mrtZk9e7acTqe1RUdHl38hAABcJ6ifAAB4T5UO8JMmTdJTTz2lBx54QO3bt9ewYcP05JNPavbs2ZKkyMhISSpyJT0jI8O6Kh8ZGam8vDxlZmaW2KY4U6dOlcvlsrYTJ054cmkAAFRL1E8AALynSgf4c+fOqUYN9yn6+PhYXyPXtGlTRUZGKjEx0Tqel5enzZs3q2vXrpKkmJgY+fn5ubVJS0vTgQMHrDbFCQgIUO3atd02AABwZdRPAAC8p0q/B/7ee+/Viy++qEaNGqlt27bau3evFixYoIcffljShZfOx8fHa9asWWrevLmaN2+uWbNmKSgoSEOHDpUkOZ1OxcXFacKECQoLC1NoaKgmTpyo9u3bW59KDwAAAABAVVelA/zixYs1ffp0jRo1ShkZGYqKitLIkSP1zDPPWG0mT56snJwcjRo1SpmZmerSpYvWr1+vkJAQq83ChQvl6+urwYMHKycnRz169FBCQoJ8fHwqY1kAAAAAAJSZwxhjKnsSdpCVlSWn0ymXy6U7n1/j0b6T5g33aH8AAFQVl9ZPXk4PAEDFVOn3wAMAAAAAgAvKFeDvuusunT59usj+rKws3XXXXRWdEwAAAAAAuEy5AvymTZuUl5dXZP8vv/yizz//vMKTAgAAAAAA7sr0IXb79u2z/v2vf/3L7fvXCwoKtG7dOt1www2emx0AAAAAAJBUxgB/8803y+FwyOFwFPtS+cDAQC1evNhjkwMAAAAAABeUKcCnpqbKGKMbb7xRO3fuVP369a1j/v7+Cg8P56vZAAAAAADwgjIF+MaNG0uSCgsLvTIZAAAAAABQvDIF+EsdPnxYmzZtUkZGRpFA/8wzz1R4YgAAAAAA4D/KFeBfe+01Pf7446pXr54iIyPlcDisYw6HgwAPAAAAAICHlSvAv/DCC3rxxRc1ZcoUT88HAAAAAAAUo1zfA5+ZmalBgwZ5ei4AAAAAAKAE5QrwgwYN0vr16z09FwAAAAAAUIJyvYS+WbNmmj59unbs2KH27dvLz8/P7fi4ceM8MjkAAAAAAHCBwxhjynqnpk2bltyhw6GjR49WaFJVUVZWlpxOp1wul+58fo1H+06aN9yj/QEAUFVcWj9r165d2dMBAMDWynUFPjU11dPzAAAAAAAAV1Cu98ADAAAAAIBrq1xX4B9++OErHn/99dfLNRkAAAAAAFC8cgX4zMxMt9v5+fk6cOCATp8+rbvuussjEwMAAAAAAP9RrgC/evXqIvsKCws1atQo3XjjjRWeFAAAAAAAcOex98DXqFFDTz75pBYuXOipLgEAAAAAwP/Pox9i9+233+r8+fOe7BIAAAAAAKicL6EfP368221jjNLS0vTxxx9rxIgRHpkYAAAAAAD4j3IF+L1797rdrlGjhurXr6/58+df9RPqAQAAAABA2ZUrwG/cuNHT8wAAAAAAAFdQrgB/0alTp3To0CE5HA61aNFC9evX99S8AAAAAADAJcr1IXZnz57Vww8/rAYNGuiOO+7Qr3/9a0VFRSkuLk7nzp3z9BwBAAAAALjulSvAjx8/Xps3b9ZHH32k06dP6/Tp0/rwww+1efNmTZgwwdNzBAAAAADguleul9C///77+tvf/qbu3btb+37zm98oMDBQgwcP1tKlSz01PwAAAAAAoHJegT937pwiIiKK7A8PD+cl9AAAAAAAeEG5AnxsbKxmzJihX375xdqXk5OjZ599VrGxsR6bHAAAAAAAuKBcL6FftGiR7r77bjVs2FAdOnSQw+FQcnKyAgICtH79ek/PEQAAAACA6165Anz79u115MgRrVy5Ul9//bWMMXrggQf04IMPKjAw0NNzBAAAAADguleuAD979mxFRETokUcecdv/+uuv69SpU5oyZYpHJgcAAAAAAC4o13vgX331VbVq1arI/rZt2+qvf/1rhScFAAAAAADclSvAp6enq0GDBkX2169fX2lpaRWeFAAAAAAAcFeuAB8dHa0vvviiyP4vvvhCUVFRFZ4UAAAAAABwV673wP/hD39QfHy88vPzddddd0mSPvvsM02ePFkTJkzw6AQBAAAAAEA5A/zkyZP1888/a9SoUcrLy5Mk1axZU1OmTNHUqVM9OkEAAAAAAFDOAO9wODRnzhxNnz5dKSkpCgwMVPPmzRUQEODp+QEAAAAAAJUzwF9Uq1Ytde7c2VNzAQAAAAAAJSjXh9gBAAAAAIBriwAPAAAAAIANEOABAAAAALABAjwAAAAAADZAgAcAAAAAwAYI8AAAAAAA2ECFvkbuWvj+++81ZcoU/eMf/1BOTo5atGih5cuXKyYmRpJkjNGzzz6rZcuWKTMzU126dNFf/vIXtW3b1uojNzdXEydO1LvvvqucnBz16NFDr7zyiho2bFhZywKuqZhJb3q0v6R5wz3aHwAAAICrq9JX4DMzM3XbbbfJz89P//jHP/Svf/1L8+fPV506daw2c+fO1YIFC7RkyRLt2rVLkZGR6tWrl86cOWO1iY+P1+rVq7Vq1Spt3bpV2dnZ6tevnwoKCiphVQAAAAAAlF2VvgI/Z84cRUdHa8WKFda+Jk2aWP82xmjRokWaNm2a7r//fknSG2+8oYiICL3zzjsaOXKkXC6Xli9frrfeeks9e/aUJK1cuVLR0dHasGGD+vTpc03XBAAAAABAeVTpK/Br165Vp06dNGjQIIWHh6tjx4567bXXrOOpqalKT09X7969rX0BAQHq1q2btm3bJklKSkpSfn6+W5uoqCi1a9fOagMAAAAAQFVXpQP80aNHtXTpUjVv3lyffvqpHnvsMY0bN05vvnnh/bzp6emSpIiICLf7RUREWMfS09Pl7++vunXrltimOLm5ucrKynLbAADAlVE/AQDwniod4AsLC/WrX/1Ks2bNUseOHTVy5Eg98sgjWrp0qVs7h8PhdtsYU2Tf5a7WZvbs2XI6ndYWHR1d/oUAAHCdoH4CAOA9VTrAN2jQQG3atHHb17p1ax0/flySFBkZKUlFrqRnZGRYV+UjIyOVl5enzMzMEtsUZ+rUqXK5XNZ24sSJCq8HAIDqjvoJAID3VOkAf9ttt+nQoUNu+w4fPqzGjRtLkpo2barIyEglJiZax/Py8rR582Z17dpVkhQTEyM/Pz+3NmlpaTpw4IDVpjgBAQGqXbu22wYAAK6M+gkAgPdU6U+hf/LJJ9W1a1fNmjVLgwcP1s6dO7Vs2TItW7ZM0oWXzsfHx2vWrFlq3ry5mjdvrlmzZikoKEhDhw6VJDmdTsXFxWnChAkKCwtTaGioJk6cqPbt21ufSg8AAAAAQFVXpQN8586dtXr1ak2dOlXPPfecmjZtqkWLFunBBx+02kyePFk5OTkaNWqUMjMz1aVLF61fv14hISFWm4ULF8rX11eDBw9WTk6OevTooYSEBPn4+FTGsgAAAAAAKDOHMcZU9iTsICsrS06nUy6XS3c+v8ajfSfNG+7R/oDLxUx606P98TMLoLQurZ+8nB4AgIqp0u+BBwAAAAAAFxDgAQAAAACwAQI8AAAAAAA2QIAHAAAAAMAGCPAAAAAAANgAAR4AAAAAABsgwAMAAAAAYAMEeAAAAAAAbIAADwAAAACADRDgAQAAAACwAQI8AAAAAAA2QIAHAAAAAMAGCPAAAAAAANgAAR4AAAAAABsgwAMAAAAAYAMEeAAAAAAAbIAADwAAAACADRDgAQAAAACwAQI8AAAAAAA2QIAHAAAAAMAGCPAAAAAAANgAAR4AAAAAABsgwAMAAAAAYAMEeAAAAAAAbIAADwAAAACADRDgAQAAAACwAQI8AAAAAAA2QIAHAAAAAMAGCPAAAAAAANgAAR4AAAAAABsgwAMAAAAAYAMEeAAAAAAAbIAADwAAAACADRDgAQAAAACwAQI8AAAAAAA2QIAHAAAAAMAGCPAAAAAAANgAAR4AAAAAABsgwAMAAAAAYAMEeAAAAAAAbIAADwAAAACADfhW9gQAAAAA4GpiJr3psb6S5g33WF/AtcQVeAAAAAAAbIAADwAAAACADRDgAQAAAACwAQI8AAAAAAA2YKsAP3v2bDkcDsXHx1v7jDGaOXOmoqKiFBgYqO7du+vgwYNu98vNzdXYsWNVr149BQcHq3///jp58uQ1nj0AAAAAAOVnmwC/a9cuLVu2TDfddJPb/rlz52rBggVasmSJdu3apcjISPXq1Utnzpyx2sTHx2v16tVatWqVtm7dquzsbPXr108FBQXXehkAAAAAAJSLLQJ8dna2HnzwQb322muqW7eutd8Yo0WLFmnatGm6//771a5dO73xxhs6d+6c3nnnHUmSy+XS8uXLNX/+fPXs2VMdO3bUypUrtX//fm3YsKGylgQAAAAAQJnYIsCPHj1a99xzj3r27Om2PzU1Venp6erdu7e1LyAgQN26ddO2bdskSUlJScrPz3drExUVpXbt2lltAAAAAACo6nwrewJXs2rVKu3Zs0e7du0qciw9PV2SFBER4bY/IiJCx44ds9r4+/u7Xbm/2Obi/YuTm5ur3Nxc63ZWVla51wAAwPWC+gkAgPdU6SvwJ06c0BNPPKGVK1eqZs2aJbZzOBxut40xRfZd7mptZs+eLafTaW3R0dFlmzwAANch6icAAN5TpQN8UlKSMjIyFBMTI19fX/n6+mrz5s3685//LF9fX+vK++VX0jMyMqxjkZGRysvLU2ZmZoltijN16lS5XC5rO3HihIdXBwBA9UP9BADAe6p0gO/Ro4f279+v5ORka+vUqZMefPBBJScn68Ybb1RkZKQSExOt++Tl5Wnz5s3q2rWrJCkmJkZ+fn5ubdLS0nTgwAGrTXECAgJUu3Zttw0AAFwZ9RMAAO+p0u+BDwkJUbt27dz2BQcHKywszNofHx+vWbNmqXnz5mrevLlmzZqloKAgDR06VJLkdDoVFxenCRMmKCwsTKGhoZo4caLat29f5EPxAAAAAACoqqp0gC+NyZMnKycnR6NGjVJmZqa6dOmi9evXKyQkxGqzcOFC+fr6avDgwcrJyVGPHj2UkJAgHx+fSpw5AAAAAAClZ7sAv2nTJrfbDodDM2fO1MyZM0u8T82aNbV48WItXrzYu5MDAAAAAMBLqvR74AEAAAAAwAUEeAAAAAAAbIAADwAAAACADRDgAQAAAACwAQI8AAAAAAA2QIAHAAAAAMAGCPAAAAAAANgAAR4AAAAAABvwrewJAMD1JGbSmx7tL2necI/2BwAAgKqLK/AAAAAAANgAV+ABAMA1w6tQAAAoP67AAwAAAABgAwR4AAAAAABsgAAPAAAAAIANEOABAAAAALABAjwAAAAAADZAgAcAAAAAwAYI8AAAAAAA2AABHgAAAAAAGyDAAwAAAABgAwR4AAAAAABsgAAPAAAAAIANEOABAAAAALABAjwAAAAAADZAgAcAAAAAwAYI8AAAAAAA2AABHgAAAAAAGyDAAwAAAABgAwR4AAAAAABsgAAPAAAAAIANEOABAAAAALABAjwAAAAAADZAgAcAAAAAwAYI8AAAAAAA2AABHgAAAAAAG/Ct7AkAAAAAVVXMpDc92l/SvOEe7Q/A9YUr8AAAAAAA2AABHgAAAAAAGyDAAwAAAABgAwR4AAAAAABsgAAPAAAAAIANEOABAAAAALABAjwAAAAAADZAgAcAAAAAwAYI8AAAAAAA2AABHgAAAAAAG6jSAX727Nnq3LmzQkJCFB4eroEDB+rQoUNubYwxmjlzpqKiohQYGKju3bvr4MGDbm1yc3M1duxY1atXT8HBwerfv79Onjx5LZcCAAAAAECFVOkAv3nzZo0ePVo7duxQYmKizp8/r969e+vs2bNWm7lz52rBggVasmSJdu3apcjISPXq1Utnzpyx2sTHx2v16tVatWqVtm7dquzsbPXr108FBQWVsSwAAAAAAMrMt7IncCXr1q1zu71ixQqFh4crKSlJd9xxh4wxWrRokaZNm6b7779fkvTGG28oIiJC77zzjkaOHCmXy6Xly5frrbfeUs+ePSVJK1euVHR0tDZs2KA+ffpc83UBAAAAAFBWVfoK/OVcLpckKTQ0VJKUmpqq9PR09e7d22oTEBCgbt26adu2bZKkpKQk5efnu7WJiopSu3btrDYAAAAAAFR1VfoK/KWMMRo/frxuv/12tWvXTpKUnp4uSYqIiHBrGxERoWPHjllt/P39Vbdu3SJtLt6/OLm5ucrNzbVuZ2VleWQdAABUZ9RPAAC8xzZX4MeMGaN9+/bp3XffLXLM4XC43TbGFNl3uau1mT17tpxOp7VFR0eXb+IAAFxHqJ8AAHiPLa7Ajx07VmvXrtWWLVvUsGFDa39kZKSkC1fZGzRoYO3PyMiwrspHRkYqLy9PmZmZblfhMzIy1LVr1xLHnDp1qsaPH2/dzsrK4kkIAEiKmfSmR/tLmjfco/2hclE/AQDwnip9Bd4YozFjxuiDDz7QP//5TzVt2tTteNOmTRUZGanExERrX15enjZv3myF85iYGPn5+bm1SUtL04EDB64Y4AMCAlS7dm23DQAAXBn1EwAA76nSV+BHjx6td955Rx9++KFCQkKs96w7nU4FBgbK4XAoPj5es2bNUvPmzdW8eXPNmjVLQUFBGjp0qNU2Li5OEyZMUFhYmEJDQzVx4kS1b9/e+lR6AAAAAACquiod4JcuXSpJ6t69u9v+FStW6KGHHpIkTZ48WTk5ORo1apQyMzPVpUsXrV+/XiEhIVb7hQsXytfXV4MHD1ZOTo569OihhIQE+fj4XKulAAAAAABQIVU6wBtjrtrG4XBo5syZmjlzZoltatasqcWLF2vx4sUenB0AAAAAANdOlX4PPAAAAAAAuIAADwAAAACADRDgAQAAAACwAQI8AAAAAAA2QIAHAAAAAMAGCPAAAAAAANgAAR4AAAAAABsgwAMAAAAAYAO+lT0BAAAuFzPpTY/2lzRvuEf7AwAAqAxcgQcAAAAAwAYI8AAAAAAA2AABHgAAAAAAGyDAAwAAAABgAwR4AAAAAABsgAAPAAAAAIANEOABAAAAALABAjwAAAAAADZAgAcAAAAAwAZ8K3sCAAAAAHA9iJn0pkf7S5o33KP9oerjCjwAAAAAADZAgAcAAAAAwAYI8AAAAAAA2AABHgAAAAAAGyDAAwAAAABgAwR4AAAAAABsgAAPAAAAAIANEOABAAAAALAB38qeAAAAANzFTHrTo/0lzRvu0f4AAJWDAA/AI3iyCQAAAHgXL6EHAAAAAMAGCPAAAAAAANgAAR4AAAAAABsgwAMAAAAAYAN8iB0AAAAAVBOe/GBhPlS46uEKPAAAAAAANsAVeHgNXysGAAAAAJ7DFXgAAAAAAGyAAA8AAAAAgA3wEnoAqGb48BoAsA/ecgigLLgCDwAAAACADXAFHgBwXeKqFwAAsBsCPFDJCBEAAACVj+dksAMCPAAAXsATQQAA4Gm8Bx4AAAAAABvgCjxwFVxFAwAAAFAVEOABAAAAAKXCxa3KdV29hP6VV15R06ZNVbNmTcXExOjzzz+v7CkBAAAAAFAq180V+Pfee0/x8fF65ZVXdNttt+nVV1/V3XffrX/9619q1KhRZU+vUnjyr2f85QwAAAAAvOu6uQK/YMECxcXF6Q9/+INat26tRYsWKTo6WkuXLq3sqQEAAAAAcFXXxRX4vLw8JSUl6amnnnLb37t3b23btq2SZgVP4D04AABUTdeiRvM8AMD15roI8P/+979VUFCgiIgIt/0RERFKT08v9j65ubnKzc21brtcLklSVlaWCnJzPDq/rKysIvvuePpdj46x5YXfFdnnyXUUt4ZrcZ6qwxjVYQ3XaozqwG7/F9X556k6PLa9WStCQkLkcDjK3MeV6qdkv/NSXP20W42WKuc8VYfH8bUYw27/11L1eFxU158nT49RWWu4Fj9P3lTeGloq5jrw/fffG0lm27ZtbvtfeOEF07Jly2LvM2PGDCOJjY2NjY3tutxcLle5ai71k42NjY3tet/KW0NLw2GMMarm8vLyFBQUpP/3//6f7rvvPmv/E088oeTkZG3evLnIfS6/glBYWKiff/5ZYWFhpfprSlZWlqKjo3XixAnVrl3bMwuphmNUhzVcizGqwxquxRjVYQ3VZYzqsIZrMUZVXoOnrsCXtX5WZM6lVZXP+/U2RnVYw7UYozqs4VqMUR3WUF3GqA5rqMgY3rwCf128hN7f318xMTFKTEx0C/CJiYkaMGBAsfcJCAhQQECA2746deqUeezatWt77QeqOo1RHdZwLcaoDmu4FmNUhzVUlzGqwxquxRjVYQ0Xeap+Spz362mM6rCGazFGdVjDtRijOqyhuoxRHdZwrcYoresiwEvS+PHjNWzYMHXq1EmxsbFatmyZjh8/rscee6yypwYAAAAAwFVdNwF+yJAh+umnn/Tcc88pLS1N7dq10yeffKLGjRtX9tQAAAAAALiq6ybAS9KoUaM0atSoazJWQECAZsyYUeRlhIxxbfuvLmNUhzVcizGqwxqqyxjVYQ3XYozqsAZv4LxfP2NUhzVcizGqwxquxRjVYQ3VZYzqsIZrNUZZXRcfYgcAAAAAgN3VqOwJAAAAAACAqyPAAwAAAABgAwR4AAAAAABsgADvYVu2bNG9996rqKgoORwOrVmzxqP9z549W507d1ZISIjCw8M1cOBAHTp0yKNjzJw5Uw6Hw22LjIz06BhNmjQpMobD4dDo0aPL3efVzr0xRjNnzlRUVJQCAwPVvXt3HTx40GP9P/TQQ0XWc+utt3p0DZKUkpKi/v37y+l0KiQkRLfeequOHz/usTGK+39xOByaN2+eR/rPzs7WmDFj1LBhQwUGBqp169ZaunRpqecvlf5xUN5zVZr+K3qeli5dqptuusn6XtHY2Fj94x//sI7/+OOPeuihhxQVFaWgoCD17dtXR44cKVXfJa3J4XAoPj5ekpSfn68pU6aoffv2Cg4OVlRUlIYPH64ffvjBY2NIF36ftGrVSsHBwapbt6569uypL7/80qNjXGrkyJFyOBxatGhRqfu82u+8Dz74QH369FG9evXkcDiUnJxc5nlfbYyKnqer9V/Rn9drhRpaOp6uod6un6UZww411Nv1szRjVLSGert+lnYMaujVx5A8W0Opn+Ufo6rVUAK8h509e1YdOnTQkiVLvNL/5s2bNXr0aO3YsUOJiYk6f/68evfurbNnz3p0nLZt2yotLc3a9u/f79H+d+3a5dZ/YmKiJGnQoEHl7vNq537u3LlasGCBlixZol27dikyMlK9evXSmTNnPNK/JPXt29dtXZ988olH1/Dtt9/q9ttvV6tWrbRp0yZ99dVXmj59umrWrOmxMS6df1paml5//XU5HA7993//t0f6f/LJJ7Vu3TqtXLlSKSkpevLJJzV27Fh9+OGHpV5DaR4HFTlXpem/ouepYcOGeumll7R7927t3r1bd911lwYMGKCDBw/KGKOBAwfq6NGj+vDDD7V37141btxYPXv2LNdjfdeuXVq2bJluuukma9+5c+e0Z88eTZ8+XXv27NEHH3ygw4cPq3///mXuv6QxJKlFixZasmSJ9u/fr61bt6pJkybq3bu3Tp065bExLlqzZo2+/PJLRUVFlbnvK/3OO3v2rG677Ta99NJLZe63tGN44jxdqf+K/rxeK9TQ0vF0DfV2/SzNGFLVr6Herp+lGaOiNdTb9bO0Y1BDrz6G5LkaSv2s2BhVroYaeI0ks3r1aq+OkZGRYSSZzZs3e6zPGTNmmA4dOnisv9J44oknzH/913+ZwsJCj/R3+bkvLCw0kZGR5qWXXrL2/fLLL8bpdJq//vWvFe7fGGNGjBhhBgwYUM4Zl26MIUOGmN///vdeHeNyAwYMMHfddZfH+m/btq157rnn3Pb96le/Mk8//XS5xjCm+MeBJ89VaR5nFTlPF9WtW9f87//+rzl06JCRZA4cOGAdO3/+vAkNDTWvvfZamfo8c+aMad68uUlMTDTdunUzTzzxRIltd+7caSSZY8eOeW0Ml8tlJJkNGzZ4dIyTJ0+aG264wRw4cMA0btzYLFy4sNR9l/Z3XmpqqpFk9u7dW6a5l2WMi8p6nsravyd+Xr2NGlp6nqyh3q6fxY1hjP1qqLfrZ0ljeLqGert+ljTG5aih3quh1E/Pj1HZNZQr8DbncrkkSaGhoR7t98iRI4qKilLTpk31wAMP6OjRox7t/1J5eXlauXKlHn74YTkcDq+MkZqaqvT0dPXu3dvaFxAQoG7dumnbtm0eG2fTpk0KDw9XixYt9MgjjygjI8NjfRcWFurjjz9WixYt1KdPH4WHh6tLly4ef4nppX788Ud9/PHHiouL81ift99+u9auXavvv/9exhht3LhRhw8fVp8+fcrd5+WPA0+fq6s9zip6ngoKCrRq1SqdPXtWsbGxys3NlSS3qx0+Pj7y9/fX1q1by9T36NGjdc8996hnz55XbetyueRwOFSnTh2vjJGXl6dly5bJ6XSqQ4cOHhujsLBQw4YN06RJk9S2bdsy9XvRtfidV9oxynueStu/Nx7XdkUNvbprVT+l6lVDvfU483QN9Xb9LG6My1FDvVtDqZ+eHaNK1NBK+9PBdUBevnpQWFho7r33XnP77bd7tN9PPvnE/O1vfzP79u2z/loXERFh/v3vf3t0nIvee+894+PjY77//nuP9Xn5uf/iiy+MpCJjPPLII6Z3794V7t8YY1atWmX+/ve/m/3795u1a9eaDh06mLZt25pffvmlPEsoMkZaWpqRZIKCgsyCBQvM3r17zezZs43D4TCbNm3yyBiXmzNnjqlbt67JycnxWP+5ublm+PDhRpLx9fU1/v7+5s033yxX/8YU/zjw5LkqzeOsvOdp3759Jjg42Pj4+Bin02k+/vhjY4wxeXl5pnHjxmbQoEHm559/Nrm5uWb27NlGUpl+Xt99913Trl07a15X+st+Tk6OiYmJMQ8++GCZ1lCaMT766CMTHBxsHA6HiYqKMjt37vToGLNmzTK9evWyrj6W9QpCaX/nVeQKQmnGqMh5Ksvv7Yo+rq8VamjpeLqGert+FjeGMfarod6unyWN4cka6u36WdIYl6OGeq+GUj89uw5jqkYNJcB7kbeffIwaNco0btzYnDhxwmtjGGNMdna2iYiIMPPnz/dK/7179zb9+vXzaJ8lPQH54Ycf3Nr94Q9/MH369Klw/8X54YcfjJ+fn3n//ffL3H9xY3z//fdGkvnd737n1u7ee+81DzzwgEfGuFzLli3NmDFjytV3Sf3PmzfPtGjRwqxdu9Z89dVXZvHixaZWrVomMTGxXGMU9zjw5LkqzeOsvOcpNzfXHDlyxOzatcs89dRTpl69eubgwYPGGGN2795tOnToYCQZHx8f06dPH3P33Xebu+++u1R9Hz9+3ISHh5vk5GRrX0lPPvLy8syAAQNMx44djcvlKvX8SztGdna2OXLkiNm+fbt5+OGHTZMmTcyPP/7okTF2795tIiIi3MJFWZ+AXK6k33kVeQJSmjEqcp5K0/9FFX1cXyvU0NLxdA31dv0sboziVPUa6u36WdIYnqyh3q6fJY1xOWqod2oo9bN89bOkMS6qCjWUAO9F3nzyMWbMGNOwYUNz9OhRr/R/uZ49e5rHHnvM4/1+9913pkaNGmbNmjUe7ffyc//tt98aSWbPnj1u7fr372+GDx9e4f5L0qxZM7f3DVZkjNzcXOPr62uef/55t3aTJ082Xbt29cgYl9qyZYuR5PaLv6L9nzt3zvj5+Zm///3vbu3i4uLK9USwpMeBp85VaR5nnjhPF/Xo0cM8+uijbvtOnz5tMjIyjDHG3HLLLWbUqFGl6mv16tXWE5eLmyTjcDiMj4+POX/+vDHmwhOPgQMHmptuuqnMVwhLO8blmjVrZmbNmuWRMV5++WXr35cer1GjhmncuHGZ1nOp4n7nefIJSEljXKos56m0/Xvy59XbqKFX540a6u36WdwYJanKNdTb9bO4MTxZQ71dP680xqWood6rodTP8tfPksaoKjXUt6wvuUflMsZo7NixWr16tTZt2qSmTZt6fczc3FylpKTo17/+tcf7XrFihcLDw3XPPfd4vO9LNW3aVJGRkUpMTFTHjh0lXXifzObNmzVnzhyvjPnTTz/pxIkTatCggUf68/f3V+fOnYt8Fcvhw4fVuHFjj4xxqeXLlysmJqbM7yG6kvz8fOXn56tGDfeP3/Dx8VFhYWGp+7na46Ci56osjzNPnidjjPXevYucTqekC+/N2r17t55//vlS9dWjR48in3z9P//zP2rVqpWmTJkiHx8f5efna/DgwTpy5Ig2btyosLCwMs23NGMUp7h1lneMBg0aFHnvZ58+fTRs2DD9z//8TxlW8x/e/J1XljHKcp5K2783Htd2Qg0tu8qon5K9a6i3HmeeqKHerp+lGeNS1FDv1VDqZ/nq55XGqDI1tHL+blB9nTlzxuzdu9fs3bvXSLLeP1TWT6QsyeOPP26cTqfZtGmTSUtLs7Zz5855pH9jjJkwYYLZtGmTOXr0qNmxY4fp16+fCQkJMd99953HxjDGmIKCAtOoUSMzZcoUj/R3tXP/0ksvGafTaT744AOzf/9+87vf/c40aNDAZGVlVbj/M2fOmAkTJpht27aZ1NRUs3HjRhMbG2tuuOGGUvdfmjV88MEHxs/PzyxbtswcOXLELF682Pj4+JjPP//cY2MYc+ETPIOCgszSpUtL3W9p++/WrZtp27at2bhxozl69KhZsWKFqVmzpnnllVdKPUZpHgcVOVelfZxV5DxNnTrVbNmyxaSmppp9+/aZP/7xj6ZGjRpm/fr1xhhj/u///s9s3LjRfPvtt2bNmjWmcePG5v777y/zOJe69KVz+fn5pn///qZhw4YmOTnZbZ25ubkeGSM7O9tMnTrVbN++3Xz33XcmKSnJxMXFmYCAALdPB67IGMUp60sAr/Y776effjJ79+41H3/8sZFkVq1aZfbu3WvS0tI8MoYnzlNpfm9X5Of1WqGGlp4na6i36+fVxrBLDfV2/SzNGBWtod6un6Udwxhq6NXG8EYNpX6Wbx3GVK0aSoD3sI0bNxpJRbYRI0Z4pP/i+pZkVqxY4ZH+jbnw9SENGjQwfn5+Jioqytx///3We4o86dNPPzWSzKFDhzzS39XOfWFhoZkxY4aJjIw0AQEB5o477jD79+/3SP/nzp0zvXv3NvXr1zd+fn6mUaNGZsSIEeb48eMeXYMxxixfvtw0a9bM1KxZ03To0KHML50szRivvvqqCQwMNKdPny5T36XpPy0tzTz00EMmKirK1KxZ07Rs2dLMnz+/TF9/VNrHQXnPVWn7r8h5evjhh03jxo2Nv7+/qV+/vunRo4f1xMMYY/70pz+Zhg0bWj9PTz/9dIWeFBjjXrgvvpytuG3jxo0eGSMnJ8fcd999Jioqyvj7+5sGDRqY/v37l/nDZa40RnHK+gTkar/zVqxYUex5mjFjhkfG8MR5Ks3v7Yr8vF4r1NDS82QN9Xb9vNoYdqmh3q6fpRmjojXU2/WzLGNQQ688hjdqKPWzfOswpmrVUIcxxggAAAAAAFRpfA88AAAAAAA2QIAHAAAAAMAGCPAAAAAAANgAAR4AAAAAABsgwAMAAAAAYAMEeAAAAAAAbIAADwAAAACADRDgAQAAAACwAQI8gBLNnDlTN998s3X7oYce0sCBA70+rsPh0Jo1a7w+zrVy+XkEAFR/1FDPoIYC7gjwgE1t27ZNPj4+6tu37zUb809/+pMSEhI81l9JRTktLU133323x8YBAOBS1FAAdkWAB2zq9ddf19ixY7V161YdP378mozpdDpVp04dr48TGRmpgIAAr48DALg+UUMB2BUBHrChs2fP6v/+7//0+OOPq1+/fkX+or9p0yY5HA59/PHH6tChg2rWrKkuXbpo//79VpuEhATVqVNHa9asUYsWLVSzZk316tVLJ06cKHHcy1/+V1hYqDlz5qhZs2YKCAhQo0aN9OKLL1rHp0yZohYtWigoKEg33nijpk+frvz8fGv8Z599Vl999ZUcDoccDoe1jstf/rd//37dddddCgwMVFhYmB599FFlZ2cXmdfLL7+sBg0aKCwsTKNHj7bGKs7FKxevv/66GjVqpFq1aunxxx9XQUGB5s6dq8jISIWHh7utR5KOHz+uAQMGqFatWqpdu7YGDx6sH3/80a3NSy+9pIiICIWEhCguLk6//PJLkfFXrFih1q1bq2bNmmrVqpVeeeWVEucKAPAcaig1FLA1A8B2li9fbjp16mSMMeajjz4yTZo0MYWFhdbxjRs3GkmmdevWZv369Wbfvn2mX79+pkmTJiYvL88YY8yKFSuMn5+f6dSpk9m2bZvZvXu3ueWWW0zXrl2tfmbMmGE6dOhg3R4xYoQZMGCAdXvy5Mmmbt26JiEhwXzzzTfm888/N6+99pp1/PnnnzdffPGFSU1NNWvXrjURERFmzpw5xhhjzp07ZyZMmGDatm1r0tLSTFpamjl37pwxxhhJZvXq1cYYY86ePWuioqLM/fffb/bv328+++wz07RpUzNixAi3edWuXds89thjJiUlxXz00UcmKCjILFu2rMRzOGPGDFOrVi3z29/+1hw8eNCsXbvW+Pv7mz59+pixY8ear7/+2rz++utGktm+fbsxxpjCwkLTsWNHc/vtt5vdu3ebHTt2mF/96lemW7duVr/vvfee8ff3N6+99pr5+uuvzbRp00xISIjbeVy2bJlp0KCBef/9983Ro0fN+++/b0JDQ01CQsIV/tcBAJ5ADaWGAnZGgAdsqGvXrmbRokXGGGPy8/NNvXr1TGJionX84pOPVatWWft++uknExgYaN577z1jzIUnH5LMjh07rDYpKSlGkvnyyy+NMVd+8pGVlWUCAgLcnmxczdy5c01MTIx1+/L+L7r0yceyZctM3bp1TXZ2tnX8448/NjVq1DDp6enWvBo3bmzOnz9vtRk0aJAZMmRIiXOZMWOGCQoKMllZWda+Pn36mCZNmpiCggJrX8uWLc3s2bONMcasX7/e+Pj4mOPHj1vHDx48aCSZnTt3GmOMiY2NNY899pjbWF26dHFbZ3R0tHnnnXfc2jz//PMmNja2xPkCADyDGkoNBeyMl9ADNnPo0CHt3LlTDzzwgCTJ19dXQ4YM0euvv16kbWxsrPXv0NBQtWzZUikpKdY+X19fderUybrdqlUr1alTx61NSVJSUpSbm6sePXqU2OZvf/ubbr/9dkVGRqpWrVqaPn16md9rmJKSog4dOig4ONjad9ttt6mwsFCHDh2y9rVt21Y+Pj7W7QYNGigjI+OKfTdp0kQhISHW7YiICLVp00Y1atRw23exn5SUFEVHRys6Oto63qZNG7dzlpKS4nbeJff/h1OnTunEiROKi4tTrVq1rO2FF17Qt99+W6pzAgAoH2ooNRSwO9/KngCAslm+fLnOnz+vG264wdpnjJGfn58yMzNVt27dK97f4XBc8XZJ+y4XGBh4xeM7duzQAw88oGeffVZ9+vSR0+nUqlWrNH/+/Kv2fSljTInzuXS/n59fkWOFhYVX7Lu4+1ypn5LmcqU5Xu5iX6+99pq6dOniduzSJ08AAM+jhhY/T2ooYB9cgQds5Pz583rzzTc1f/58JScnW9tXX32lxo0b6+2333Zrv2PHDuvfmZmZOnz4sFq1auXW3+7du63bhw4d0unTp93alKR58+YKDAzUZ599VuzxL774Qo0bN9a0adPUqVMnNW/eXMeOHXNr4+/vr4KCgiuO06ZNGyUnJ+vs2bNufdeoUUMtWrS46jw9qU2bNjp+/LjbhxT961//ksvlUuvWrSVJrVu3djvvkvv/Q0REhG644QYdPXpUzZo1c9uaNm16bRYCANchauh/+qaGAvbFFXjARv7+978rMzNTcXFxcjqdbsd++9vfavny5RozZoy177nnnlNYWJgiIiI0bdo01atXz+0TcP38/DR27Fj9+c9/lp+fn8aMGaNbb71Vt9xyy1XnUrNmTU2ZMkWTJ0+Wv7+/brvtNp06dUoHDx5UXFycmjVrpuPHj2vVqlXq3LmzPv74Y61evdqtjyZNmig1NVXJyclq2LChQkJCinz1zYMPPqgZM2ZoxIgRmjlzpk6dOqWxY8dq2LBhioiIKMdZLL+ePXvqpptu0oMPPqhFixbp/PnzGjVqlLp162a9jPKJJ57QiBEj1KlTJ91+++16++23dfDgQd14441WPzNnztS4ceNUu3Zt3X333crNzdXu3buVmZmp8ePHX9M1AcD1ghpKDQWqA67AAzayfPly9ezZs8gTD0n67//+byUnJ2vPnj3WvpdeeklPPPGEYmJilJaWprVr18rf3986HhQUpClTpmjo0KGKjY1VYGCgVq1aVer5TJ8+XRMmTNAzzzyj1q1ba8iQIdZ73QYMGKAnn3xSY8aM0c0336xt27Zp+vTpRebct29f3Xnnnapfv77efffdImMEBQXp008/1c8//6zOnTvrt7/9rXr06KElS5aUep6ecvGreerWras77rhDPXv21I033qj33nvPajNkyBA988wzmjJlimJiYnTs2DE9/vjjbv384Q9/0P/+7/8qISFB7du3V7du3ZSQkMDVAwDwImooNRSoDhzGGFPZkwDgWZs2bdKdd96pzMxM1alTp9g2CQkJio+P1+nTp6/p3AAAqMqooQCqMq7AAwAAAABgAwR4AAAAAABsgJfQAwAAAABgA1yBBwAAAADABgjwAAAAAADYAAEeAAAAAAAbIMADAAAAAGADBHgAAAAAAGyAAA8AAAAAgA0Q4AEAAAAAsAECPAAAAAAANkCABwAAAADABv4/gh+EV9veQ5EAAAAASUVORK5CYII=", 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", 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", 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", 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", 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68Y9/rEmTJum9995zPrcsyzW8MSamn9fBhrn11luVlpbmdIWFhYe3EAAA4JAQiwEAODzNnsyHQiF16tRJxx13nG699Vb16tVLv/rVr5SXlydJMS3sW7dudVrr8/LyVFVVpW3bttU6TE1mz56t8vJyp9u8eXMDLxUAADgQYjEAAIen2ZN5L2OMKisr1b59e+Xl5WnJkiXOZ1VVVVqxYoUGDBggSerbt6/i4+Ndw2zZskVr1qxxhqlJOBx2fg7P7gAAQNMhFgMAcHia9Zn5n/70pxozZowKCwu1Y8cOLVy4UMuXL9crr7wiy7I0bdo03XLLLSouLlZxcbFuueUWJSYm6rzzzpMkpaWlacqUKZo+fbqysrKUmZmpGTNmqEePHho+fHhzLhoAAAAAAI2mWZP5//3vf7rgggu0ZcsWpaWlqWfPnnrllVc0YsQISdLMmTO1e/duXXrppdq2bZtOOOEELV68WCkpKc405s6dq7i4OI0fP167d+/WsGHDNH/+fAWDweZaLAAAAAAAGlWL+5355sBv2wIA0LyIxQAAHJoW98w8AAAAAAA4MJJ5AAAAAAB8plmfmQcAAACOFn2vWeAql945sZlqAuBIQMs8AAAAAAA+QzIPAAAAAIDPkMwDAAAAAOAzJPMAAAAAAPgMyTwAAAAAAD5DMg8AAAAAgM+QzAMAAAAA4DMk8wAAAAAA+AzJPAAAAAAAPkMyDwAAAACAz5DMAwAAAADgMyTzAAAAAAD4DMk8AAAAAAA+QzIPAAAAAIDPkMwDAAAAAOAzJPMAAAAAAPgMyTwAAAAAAD5DMg8AAAAAgM+QzAMAAAAA4DMk8wAAAAAA+AzJPAAAAAAAPkMyDwAAAACAz5DMAwAAAADgMyTzAAAAAAD4DMk8AAAAAAA+QzIPAAAAAIDPkMwDAAAAAOAzJPMAAAAAAPgMyTwAAAAAAD5DMg8AAAAAgM+QzAMAAAAA4DMk8wAAAAAA+AzJPAAAAAAAPkMyDwAAAACAz5DMAwAAAADgMyTzAAAAAAD4DMk8AAAAAAA+QzIPAAAAAIDPkMwDAAAAAOAzJPMAAAAAAPgMyTwAAAAAAD5DMg8AAAAAgM+QzAMAAAAA4DMk8wAAAAAA+AzJPAAAAAAAPkMyDwAAAACAz5DMAwAAAADgMyTzAAAAAAD4DMk8AAAAAAA+QzIPAAAAAIDPkMwDAAAAAOAzJPMAAAAAAPhMsybzt956q/r166eUlBTl5OTojDPO0Nq1a13DTJ48WZZluboTTzzRNUxlZaUuv/xyZWdnKykpSWPHjtWnn37alIsCAAAAAECTadZkfsWKFbrsssv0xhtvaMmSJdq7d69GjhypXbt2uYYbPXq0tmzZ4nQvv/yy6/Np06Zp0aJFWrhwoV5//XXt3LlTp512mqqrq5tycQAAAAAAaBJxzTnzV155xVV+/PHHlZOTo9LSUg0cONDpHw6HlZeXV+M0ysvLNW/ePD355JMaPny4JOmpp55SYWGhli5dqlGjRjXeAgAAAAAA0Axa1DPz5eXlkqTMzExX/+XLlysnJ0clJSX60Y9+pK1btzqflZaWas+ePRo5cqTTr6CgQN27d9eqVauapuIAAAAAADShZm2Zj2aM0dVXX62TTz5Z3bt3d/qPGTNG55xzjoqKirRhwwZdd911Gjp0qEpLSxUOh1VWVqZQKKSMjAzX9HJzc1VWVlbjvCorK1VZWemUKyoqGmehAABAjYjFAAAcnhaTzE+dOlVvv/22Xn/9dVf/c8891/m/e/fuOu6441RUVKSXXnpJZ511Vq3TM8bIsqwaP7v11lt14403NkzFAQDAISMWAwBweFrEbfaXX365/vjHP2rZsmVq06bNAYfNz89XUVGR1q1bJ0nKy8tTVVWVtm3b5hpu69atys3NrXEas2fPVnl5udNt3ry5YRYEAADUCbEYAIDD06zJvDFGU6dO1e9+9zv99a9/Vfv27Q86zpdffqnNmzcrPz9fktS3b1/Fx8dryZIlzjBbtmzRmjVrNGDAgBqnEQ6HlZqa6uoAAEDTIRYDAHB4mvU2+8suu0xPP/20/vCHPyglJcV5xj0tLU2RSEQ7d+7UnDlzdPbZZys/P18bN27UT3/6U2VnZ+vMM890hp0yZYqmT5+urKwsZWZmasaMGerRo4fzdnsAAAAAAI4kzZrMP/jgg5KkwYMHu/o//vjjmjx5soLBoN555x0tWLBA27dvV35+voYMGaJnn31WKSkpzvBz585VXFycxo8fr927d2vYsGGaP3++gsFgUy4OAAAAAABNwjLGmOauRHOrqKhQWlqaysvLuc0PAIBmQCzG0aDvNQtc5dI7JzZTTQAcCVrEC/AAAAAAAEDdkcwDAAAAAOAzJPMAAAAAAPgMyTwAAAAAAD5DMg8AAAAAgM+QzAMAAAAA4DMk8wAAAAAA+AzJPAAAAAAAPkMyDwAAAACAz5DMAwAAAADgM3HNXQEAddP3mgXO/6V3TmzGmgAAAABobrTMAwAAAADgMyTzAAAAAAD4DMk8AAAAAAA+QzIPAAAAAIDPkMwDAAAAAOAzJPMAAAAAAPgMyTwAAAAAAD5DMg8AAAAAgM+QzAMAAAAA4DMk8wAAAAAA+AzJPAAAAAAAPkMyDwAAAACAz5DMAwAAAADgMyTzAAAAAAD4DMk8AAAAAAA+QzIPAAAAAIDPkMwDAAAAAOAzJPMAAAAAAPgMyTwAAAAAAD5DMg8AAAAAgM+QzAMAAAAA4DMk8wAAAAAA+AzJPAAAAAAAPkMyDwAAAACAz5DMAwAAAADgM3HNXQEAqIu+1yxwlUvvnNhMNQEAAACaHy3zAAAAAAD4DMk8AAAAAAA+QzIPAAAAAIDPkMwDAAAAAOAzJPMAAAAAAPgMyTwAAAAAAD5DMg8AAAAAgM+QzAMAAAAA4DMk8wAAAAAA+AzJPAAAAAAAPkMyDwAAAACAz5DMAwAAAADgMyTzAAAAAAD4DMk8AAAAAAA+QzIPAAAAAIDPkMwDAAAAAOAzJPMAAAAAAPgMyTwAAAAAAD7TrMn8rbfeqn79+iklJUU5OTk644wztHbtWtcwxhjNmTNHBQUFikQiGjx4sN59913XMJWVlbr88suVnZ2tpKQkjR07Vp9++mlTLgoAAAAAAE2mWZP5FStW6LLLLtMbb7yhJUuWaO/evRo5cqR27drlDHPHHXfo7rvv1v33368333xTeXl5GjFihHbs2OEMM23aNC1atEgLFy7U66+/rp07d+q0005TdXV1cywWAAAAAACNKq45Z/7KK6+4yo8//rhycnJUWlqqgQMHyhije+65R9dee63OOussSdITTzyh3NxcPf3007r44otVXl6uefPm6cknn9Tw4cMlSU899ZQKCwu1dOlSjRo1qsmXCwAAAACAxtSinpkvLy+XJGVmZkqSNmzYoLKyMo0cOdIZJhwOa9CgQVq1apUkqbS0VHv27HENU1BQoO7duzvDAAAAAABwJGnWlvloxhhdffXVOvnkk9W9e3dJUllZmSQpNzfXNWxubq42bdrkDBMKhZSRkREzjD2+V2VlpSorK51yRUVFgy0HAAA4OGIxAACHp8W0zE+dOlVvv/22nnnmmZjPLMtylY0xMf28DjTMrbfeqrS0NKcrLCysf8UBAMAhIxYDAHB4WkQyf/nll+uPf/yjli1bpjZt2jj98/LyJCmmhX3r1q1Oa31eXp6qqqq0bdu2Wofxmj17tsrLy51u8+bNDbk4AADgIIjFAAAcnmZN5o0xmjp1qn73u9/pr3/9q9q3b+/6vH379srLy9OSJUucflVVVVqxYoUGDBggSerbt6/i4+Ndw2zZskVr1qxxhvEKh8NKTU11dQAAoOkQiwEAODzN+sz8ZZddpqefflp/+MMflJKS4rTAp6WlKRKJyLIsTZs2TbfccouKi4tVXFysW265RYmJiTrvvPOcYadMmaLp06crKytLmZmZmjFjhnr06OG83R4AAAAAgCNJsybzDz74oCRp8ODBrv6PP/64Jk+eLEmaOXOmdu/erUsvvVTbtm3TCSecoMWLFyslJcUZfu7cuYqLi9P48eO1e/duDRs2TPPnz1cwGGyqRQF8oe81C5z/S++c2Iw1AQAAAHA4mjWZN8YcdBjLsjRnzhzNmTOn1mESEhJ033336b777mvA2gEAAAAA0DK1iBfgAQAAAACAuiOZBwAAAADAZ0jmAQAAAADwGZJ5AAAAAAB8hmQeAAAAAACfIZkHAAAAAMBnSOYBAAAAAPAZknkAAAAAAHyGZB4AAAAAAJ8hmQcAAAAAwGdI5gEAAAAA8BmSeQAAAAAAfIZkHgAAAAAAnyGZBwAAAADAZ0jmAQAAAADwGZJ5AAAAAAB8hmQeAAAAAACfIZkHAAAAAMBnSOYBAAAAAPAZknkAAAAAAHyGZB4AAAAAAJ8hmQcAAAAAwGdI5gEAAAAA8BmSeQAAAAAAfIZkHgAAAAAAnyGZBwAAAADAZ0jmAQAAAADwGZJ5AAAAAAB8hmQeAAAAAACfIZkHAAAAAMBnSOYBAAAAAPAZknkAAAAAAHyGZB4AAAAAAJ8hmQcAAAAAwGdI5gEAAAAA8BmSeQAAAAAAfIZkHgAAAAAAn6lXMj906FBt3749pn9FRYWGDh16uHUCAAAAAAAHUK9kfvny5aqqqorp/8033+i111477EoBAAAAAIDaxR3KwG+//bbz/3vvvaeysjKnXF1drVdeeUWtW7duuNoBAAAAAIAYh5TM9+7dW5ZlybKsGm+nj0Qiuu+++xqscgAAAAAAINYhJfMbNmyQMUYdOnTQP//5T7Vq1cr5LBQKKScnR8FgsMErCQAAAAAAvnVIyXxRUZEkad++fY1SGQAAAAAAcHCHlMxH+/DDD7V8+XJt3bo1Jrm//vrrD7tiAAAAAACgZvVK5h999FH9+Mc/VnZ2tvLy8mRZlvOZZVkk8wAAAAAANKJ6JfM33XSTbr75Zs2aNauh6wMAAAAAAA6iXr8zv23bNp1zzjkNXRcAAAAAAFAH9UrmzznnHC1evLih6wIAAAAAAOqgXrfZd+rUSdddd53eeOMN9ejRQ/Hx8a7Pr7jiigapHAAAAAAAiFWvZP6RRx5RcnKyVqxYoRUrVrg+syyLZB4AAAAAgEZUr2R+w4YNDV0PAAAAAABQR/V6Zh4AAAAAADSferXMX3jhhQf8/LHHHqtXZQAAAAAAwMHVK5nftm2bq7xnzx6tWbNG27dv19ChQxukYgAAAAAAoGb1SuYXLVoU02/fvn269NJL1aFDh8OuFAAAAAAAqF2DPTMfCAR01VVXae7cuXUe529/+5tOP/10FRQUyLIs/f73v3d9PnnyZFmW5epOPPFE1zCVlZW6/PLLlZ2draSkJI0dO1affvppQywSAAAAAAAtUoO+AG/9+vXau3dvnYfftWuXevXqpfvvv7/WYUaPHq0tW7Y43csvv+z6fNq0aVq0aJEWLlyo119/XTt37tRpp52m6urqei8HAAAAAAAtWb1us7/66qtdZWOMtmzZopdeekmTJk2q83TGjBmjMWPGHHCYcDisvLy8Gj8rLy/XvHnz9OSTT2r48OGSpKeeekqFhYVaunSpRo0aVee6wN/6XrPAVS69c2Iz1QQAAAAAGl+9kvl///vfrnIgEFCrVq101113HfRN94dq+fLlysnJUXp6ugYNGqSbb75ZOTk5kqTS0lLt2bNHI0eOdIYvKChQ9+7dtWrVKpJ5AAAAAMARqV7J/LJlyxq6HjUaM2aMzjnnHBUVFWnDhg267rrrNHToUJWWliocDqusrEyhUEgZGRmu8XJzc1VWVlbrdCsrK1VZWemUKyoqGm0ZAABALGIxAACHp17JvO3zzz/X2rVrZVmWSkpK1KpVq4aqlyTp3HPPdf7v3r27jjvuOBUVFemll17SWWedVet4xhhZllXr57feeqtuvPHGBq0rAACoO2IxAACHp14vwNu1a5cuvPBC5efna+DAgfrOd76jgoICTZkyRV9//XVD19GRn5+voqIirVu3TpKUl5enqqqqmN+937p1q3Jzc2udzuzZs1VeXu50mzdvbrQ6AwCAWMRiAAAOT72S+auvvlorVqzQn/70J23fvl3bt2/XH/7wB61YsULTp09v6Do6vvzyS23evFn5+fmSpL59+yo+Pl5LlixxhtmyZYvWrFmjAQMG1DqdcDis1NRUVwcAAJoOsRgAgMNTr9vsX3jhBT3//PMaPHiw0++UU05RJBLR+PHj9eCDD9ZpOjt37tRHH33klDds2KDVq1crMzNTmZmZmjNnjs4++2zl5+dr48aN+ulPf6rs7GydeeaZkqS0tDRNmTJF06dPV1ZWljIzMzVjxgz16NHDebs9AAAAAABHmnol819//XWNt7Hn5OQc0m32b731loYMGeKU7Z+8mzRpkh588EG98847WrBggbZv3678/HwNGTJEzz77rFJSUpxx5s6dq7i4OI0fP167d+/WsGHDNH/+fAWDwfosGgAAAAAALV69kvn+/fvrhhtu0IIFC5SQkCBJ2r17t2688Ub179+/ztMZPHiwjDG1fv7qq68edBoJCQm67777dN9999V5vgAAAAAA+Fm9kvl77rlHY8aMUZs2bdSrVy9ZlqXVq1crHA5r8eLFDV1HAAAAAAAQpV7JfI8ePbRu3To99dRT+uCDD2SM0YQJE3T++ecrEok0dB0BAAAAAECUeiXzt956q3Jzc/WjH/3I1f+xxx7T559/rlmzZjVI5QAAAAAAQKx6/TTdww8/rC5dusT079atmx566KHDrhQAAAAAAKhdvZL5srIy57feo7Vq1Upbtmw57EoBAAAAAIDa1SuZLyws1MqVK2P6r1y5UgUFBYddKQAAAAAAULt6PTP/wx/+UNOmTdOePXs0dOhQSdJf/vIXzZw5U9OnT2/QCgIAAAAAALd6JfMzZ87UV199pUsvvVRVVVWS9v/e+6xZszR79uwGrSAAAAAAAHCrVzJvWZZuv/12XXfddXr//fcViURUXFyscDjc0PUDAAAAAAAe9UrmbcnJyerXr19D1QUAAAAAANRBvV6ABwAAAAAAms9htcwDAAAAR4O+1yxwlUvvnNhMNQGA/WiZBwAAAADAZ0jmAQAAAADwGZJ5AAAAAAB8hmQeAAAAAACfIZkHAAAAAMBnSOYBAAAAAPAZknkAAAAAAHyGZB4AAAAAAJ8hmQcAAAAAwGdI5gEAAAAA8BmSeQAAAAAAfIZkHgAAAAAAnyGZBwAAAADAZ+KauwIAAADwv77XLHCVS++c2Ew1AYCjAy3zAAAAAAD4DMk8AAAAAAA+QzIPAAAAAIDPkMwDAAAAAOAzJPMAAAAAAPgMyTwAAAAAAD5DMg8AAAAAgM+QzAMAAAAA4DMk8wAAAAAA+AzJPAAAAAAAPhPX3BUAAAAA0PL1vWaB83/pnRObsSYAJFrmAQAAAADwHZJ5AAAAAAB8hmQeAAAAAACfIZkHAAAAAMBneAEeALQA0S8VknixEAAAAA6MlnkAAAAAAHyGZB4AAAAAAJ8hmQcAAAAAwGdI5gEAAAAA8BmSeQAAAAAAfIZkHgAAAAAAnyGZBwAAAADAZ0jmAQAAAADwGZJ5AAAAAAB8hmQeAAAAAACfIZkHAAAAAMBnSOYBAAAAAPAZknkAAAAAAHymWZP5v/3tbzr99NNVUFAgy7L0+9//3vW5MUZz5sxRQUGBIpGIBg8erHfffdc1TGVlpS6//HJlZ2crKSlJY8eO1aefftqESwEAAAAAQNNq1mR+165d6tWrl+6///4aP7/jjjt099136/7779ebb76pvLw8jRgxQjt27HCGmTZtmhYtWqSFCxfq9ddf186dO3Xaaaepurq6qRYDAAAAAIAmFdecMx8zZozGjBlT42fGGN1zzz269tprddZZZ0mSnnjiCeXm5urpp5/WxRdfrPLycs2bN09PPvmkhg8fLkl66qmnVFhYqKVLl2rUqFFNtiwAAAAAADSVFvvM/IYNG1RWVqaRI0c6/cLhsAYNGqRVq1ZJkkpLS7Vnzx7XMAUFBerevbszDAAAAAAAR5pmbZk/kLKyMklSbm6uq39ubq42bdrkDBMKhZSRkREzjD1+TSorK1VZWemUKyoqGqraAACgDojFAAAcnhbbMm+zLMtVNsbE9PM62DC33nqr0tLSnK6wsLBB6goAAOqGWAwAwOFpscl8Xl6eJMW0sG/dutVprc/Ly1NVVZW2bdtW6zA1mT17tsrLy51u8+bNDVx7AABwIMRiAAAOT4tN5tu3b6+8vDwtWbLE6VdVVaUVK1ZowIABkqS+ffsqPj7eNcyWLVu0Zs0aZ5iahMNhpaamujoAANB0iMUAAByeZn1mfufOnfroo4+c8oYNG7R69WplZmaqbdu2mjZtmm655RYVFxeruLhYt9xyixITE3XeeedJktLS0jRlyhRNnz5dWVlZyszM1IwZM9SjRw/n7fYAAAAAABxpmjWZf+uttzRkyBCnfPXVV0uSJk2apPnz52vmzJnavXu3Lr30Um3btk0nnHCCFi9erJSUFGecuXPnKi4uTuPHj9fu3bs1bNgwzZ8/X8FgsMmXBwAAAACAptCsyfzgwYNljKn1c8uyNGfOHM2ZM6fWYRISEnTffffpvvvua4QaAgfX95oFzv+ld05sxpoAAAAAOFq02GfmAQAAAABAzUjmAQAAAADwGZJ5AAAAAAB8hmQeAAAAAACfIZkHAAAAAMBnSOYBAAAAAPCZZv1pOgAtHz+9BwAAALQ8tMwDAAAAAOAzJPMAAAAAAPgMyTwAAAAAAD5DMg8AAAAAgM+QzAMAAAAA4DMk8wAAAAAA+AzJPAAAAAAAPkMyDwAAAACAz5DMAwAAAADgMyTzAAAAAAD4DMk8AAAAAAA+QzIPAAAAAIDPxDV3BQDUT99rFjj/l945sRlrAgAAAKCp0TIPAAAAAIDPkMwDAAAAAOAzJPMAAAAAAPgMyTwAAAAAAD5DMg8AAAAAgM+QzAMAAAAA4DMk8wAAAAAA+AzJPAAAAAAAPhPX3BUA4C99r1ng/F9658RmrAkAAABw9CKZB45Q0Um3ROINAAAAHEm4zR4AAAAAAJ8hmQcAAAAAwGdI5gEAAAAA8BmSeQAAAAAAfIZkHgAAAAAAn+Ft9sARwvv2egAAAABHLlrmAQAAAADwGVrmARwxou9OKL1zYjPW5OjDugcAAGhatMwDAAAAAOAztMwDOCzeZ/VplQUAAAAaHy3zAAAAAAD4DC3zQDPwPl/M88YAAAAADgUt8wAAAAAA+Awt8zhq8Gw3AAAAgCMFLfMAAAAAAPgMLfNAA+P5dwAAAACNjZZ5AAAAAAB8hmQeAAAAAACfIZkHAAAAAMBnSOYBAAAAAPAZXoAHwHGk/3zfkb58AAAAOHrQMg8AAAAAgM+QzAMAAAAA4DPcZg+gwUXfzs6t7AAAAEDDo2UeAAAAAACfadHJ/Jw5c2RZlqvLy8tzPjfGaM6cOSooKFAkEtHgwYP17rvvNmONAQAAAABofC3+Nvtu3bpp6dKlTjkYDDr/33HHHbr77rs1f/58lZSU6KabbtKIESO0du1apaSkNEd14SO82RwAAACAX7X4ZD4uLs7VGm8zxuiee+7Rtddeq7POOkuS9MQTTyg3N1dPP/20Lr744qauKloYnttGfXCRB0Bj4fgCAGhILfo2e0lat26dCgoK1L59e02YMEEff/yxJGnDhg0qKyvTyJEjnWHD4bAGDRqkVatWNVd1AQAAAABodC26Zf6EE07QggULVFJSov/973+66aabNGDAAL377rsqKyuTJOXm5rrGyc3N1aZNmw443crKSlVWVjrlioqKhq88AACoFbEYAIDD06Jb5seMGaOzzz5bPXr00PDhw/XSSy9J2n87vc2yLNc4xpiYfl633nqr0tLSnK6wsLDhKw8AAGpFLAYA4PC06GTeKykpST169NC6deuc5+jtFnrb1q1bY1rrvWbPnq3y8nKn27x5c6PVuaXoe80CVwcAQHM6GmMxAAANqUXfZu9VWVmp999/X9/5znfUvn175eXlacmSJerTp48kqaqqSitWrNDtt99+wOmEw2GFw+GmqDKAZsSFK6DlIhYDaGl4eTL8pkUn8zNmzNDpp5+utm3bauvWrbrppptUUVGhSZMmybIsTZs2TbfccouKi4tVXFysW265RYmJiTrvvPOau+oAAAAAADSaFp3Mf/rpp/re976nL774Qq1atdKJJ56oN954Q0VFRZKkmTNnavfu3br00ku1bds2nXDCCVq8eHGz/sY8V/QAAAAAAI2tRSfzCxcuPODnlmVpzpw5mjNnTtNUCAAAAACAFsBXL8ADAAAAAAAk8wAAAAAA+A7JPAAAAAAAPtOin5kHmhsvNAQAAADQEpHMo9l5fwucpBkAAAAADozb7AEAAAAA8BmSeQAAAAAAfIbb7AG0CLyfAAAAAKg7WuYBAAAAAPAZknkAAAAAAHyG2+yBFog3/AMAAAA4EFrmAQAAAADwGZJ5AAAAAAB8hmQeAAAAAACf4Zl5NDp+cgwAAAAAGhYt8wAAAAAA+Awt8wDQwPg1AgAAADQ2WuYBAAAAAPAZknkAAAAAAHyGZB4AAAAAAJ/hmXkARzV+bQEAAAB+RDKPw0IiBABoSXgBJeqC7QTAkYDb7AEAAAAA8Bla5gEAAADAx7jb5OhEMg8Ah4nHTQAAANDUSObRoLgqCAAAmhLnHgCOVjwzDwAAAACAz9AyDwCHiNvqAQAA0NxI5gEAANAkvBdDuUUeAOqP2+wBAAAAAPAZknkAAAAAAHyG2+wBAACABsA7VQA0JZJ5AAAAHFFIqgEcDUjmAaAZcKIJAACAw8Ez8wAAAAAA+Awt8wDQQtF6DwDNpyUfg1ty3QA0HZJ5AMBRjd+5Blq2lpq4cuwA0NxI5gEAAI5gzZV0kuw2jLqsx5Z6wQNA4+KZeQAAAAAAfIaWeQAAgCMIrbQAcHQgmQcAnzjYCTq3tAJHJ5L3htEY69F7XG6scQ42jZa0XbTkugF+QzIPAAAA4IjCRS4cDXhmHgAAAAAAn6FlHo6WfAWzJdcNAAAAAJoayTxqxTNNwNGJi2cAgPrg3BFoWiTzR7H6nLBzkg+0HA3xkiQAqCvOAQCgZeGZeQAAAAAAfIaWeQA4inFLJACJVnc0Hu4EBRoPyTwA4IBI+IGWg/0RjcXv2xYXAHA0Ipn3gbocXDmAAQAAAPAjcpn6IZkHADQ4b1D2e4sPcDD12cbZLwAAh4MX4AEAAAAA4DO0zDcybhkB0JK0pJ+za4rjIy2f8DvOI3Ck464WoP5I5psYBx8AqJvmOl421XyJBwAA4HCQzLdAB7sKzwkggKMVrZQ40rGNAy1HTefc7KNHFr9/n0fMM/MPPPCA2rdvr4SEBPXt21evvfZac1cJAAAAAIBGcUS0zD/77LOaNm2aHnjgAZ100kl6+OGHNWbMGL333ntq27Ztc1evWbSk52IBHFla8t1BHPvQkvi9xQdA06jPz1BzfIF0hCTzd999t6ZMmaIf/vCHkqR77rlHr776qh588EHdeuutjTbfhjqhZWcEgPppycfPllw3HDq+T+DI0xD7dWP8FGtjXTRvruNYS24E8DvfJ/NVVVUqLS3VT37yE1f/kSNHatWqVYc1ba6ANY66rMdDfW9AUznS3rhKK+bBcdGu6TTVOqrPfBriXSb1OfY1xPHSbwb+7BkFw5EGOymui4Y4tqNhtOSY6XeNcXxhPzi45tymmyp2NcQ4fq1bU8aqmvg+mf/iiy9UXV2t3NxcV//c3FyVlZXVOE5lZaUqKyudcnl5uSSpoqLCNVx15W7n/4qKigOW6zJMQ43j1RjjNOfyNec68Wopy+f37aQuDjZOTfM52DRa8jppqu/ci3XSMON4NdXy1cQ7TEpKiizLOuAytQS1xeLqqv3LU9s6OpjGGudIO7541WdbO9DnNWnO/a8+47SEc4/6zIdjbv2Pn36Nzc19bDjYOA1xjG2KujXk8jVZLDY+99lnnxlJZtWqVa7+N910k+ncuXON49xwww1GEh0dHR0d3RHXlZeXN0X4PWzEYjo6Ojq6I7Vrqljs+7fZZ2dnKxgMxrTCb926Naa13jZ79myVl5c73bZt27R+/Xp98sknkqTNmzervLxcmzdvdpVr6newclON46e6sk5YJy1pHD/VlXXCOqlLOSUlRX5Q31jsp+/CT3VlnbBOWtI4fqor64R1UlO5qWKx72+zD4VC6tu3r5YsWaIzzzzT6b9kyRKNGzeuxnHC4bDC4bCrX3p6unNrRWpqqlJTU53PvOW6DNNc4/iprk01jp/q2lTj+KmuTTWOn+raVOP4qa5NNY6f6trSHW4s9tN34ae6NtU4fqprU43jp7o21Th+qmtTjeOnujbVOC25ro3N98m8JF199dW64IILdNxxx6l///565JFH9Mknn+iSSy5p7qoBAAAAANDgjohk/txzz9WXX36pn//859qyZYu6d++ul19+WUVFRc1dNQAAAAAAGtwRkcxL0qWXXqpLL730sKYRDod1ww03OLf9ect1Gaa5xvFTXVknrJOWNI6f6so6YZ3UdRw/a8nr9UjfblgnrJOWMo6f6so6YZ3UNkxTsIwxpknnCAAAAAAADovv32YPAAAAAMDRhmQeAAAAAACfIZkHAAAAAMBvzBGqoqLCXHnllaZt27YmISHB9O/f3yxbtszpFwwGjSRXl5CQYFq1ahXT/0BdUVGRiY+Pd8ppaWkmHA67hgkEAs7/cXFxMfNISEg46HzS0tIOqV6BQMDExcW5+uXl5RnLspxyMBh0lUOhUMx6iUQirnJcXFzM8nm7SCTiWuaG6CzLipmmt67esmVZruWL/t/uDrYslmWZxMTEmOWraVoHq9uhrhPvuq+pbgdbR/Xp6rJ89ZmPd5xQKBTzubdfXfaNg80nKSnJtTxxcXGu7ycQCJjMzMwajwm1fZ/1rduR1EUiEdd6sSwrZpv1rrdQKBSzbR1sW0pNTTVJSUnNvryN1aWlpcUcp6L3g0AgYHJzc13r8qKLLjL9+vVz1l1CQoIpKipyymlpaeb55583xhhzyy23GEnmyiuvNL/+9a9Nu3btTDgcNscee6y56qqrXOW//e1vjR6Ljz/+eDNhwgSnXFPMDYVCBz02e7uioiLXeuzYsaNr27IsK2Z7jP48IyPDFBYWHnQ+3rh6sM4bh+Lj42OOHampqQfcTwKBQMx+kpOTc8Bjlj2dw9k2A4FAzHlDXZbX+90dbJ8PBAIxw3jXczAYjOnnPd7UVE9vv4Ots9qW6VCmUdPnh7IO7a4uMaY+0z3Y99G9e3fXNhkMBmO20Zq6A537hkKhGr+/6LqEw2GTk5NzSMuUmZlpOnTocMjr4EjqvNtbWlpazLqOzlNq2gbqEpuTk5Nd/WqK3fXZHltK511HSUlJrmVMTEw0nTt3dq3v3NxcZ5hIJGKuv/56M2jQoAPG4sGDB8fEXW9sPtRYfMQm8+PHjzddu3Y1K1asMOvWrTM33HCDiYuLMyUlJWbFihXmzDPPdL6oRx991Kxatcrk5eUZSea4444zkkyvXr1MXFycueGGG5wvSpIpLCw0P/rRj1xf+imnnOIqd+zY0VXOzc11dgZJpmvXrubaa691DXPqqae6yieccIKR3Af0UaNGuYYZPny4q3zMMce4dqiCgoJaN1x7Y7NPYOzAf6ADo72xt23b9oDLG91lZGQYSaZ3795G+vaA36tXLyPJZGVl1fmAMGzYsJh+gwYNcpWHDBniKnfp0sVV7tevX8x8vOvVO5/CwkLXiZ5lWaZHjx6uYSZMmBCzXs8991zn87i4OHPiiSe6xjn99NNd5ZNOOsn1nQcCAdO9e/da6x/9nUcfvL3rZOjQoTVuN3Zdu3fvbn7wgx+4hrG/L7s744wzjCTn4kanTp3MxRdf7Bpm7NixNS6P3bVr1y5mnNq2YbsrKipyli8uLs6EQiEnAbDrb9fJLtvfTfSB2D4Zsfulp6cb6dv92ptUdOnSxTkmeLfllJQUI8m0b9/eSN8G086dO7vGSUlJcfZ9u/N+nyUlJeayyy5z9RsxYoSrfPzxx7vqPnr0aNd8JZmzzjqrxnVgd96yZVnmnHPOMdK3+7VdN3v5Jk2a5NpP8vPzY459dl3t+drHBnt7rOkk0K6/Pd1OnToZ6dtjUG5urrngggtc4/Ts2TNmvUn7T+YkmTZt2hhJpm/fvs4wp512mqtu3/nOd2pcr9H7jj2O3XmPDd712LNnTzNz5kxXv2OPPdZVPvnkk13lvLw8c/nll7v6effR8ePHm9tvv91ZR126dHEtmyTz05/+1Lz66qvOxd5x48aZ559/3rRp08YEAgGzaNEi065dO9OzZ08zZswYEx8fbx599FHz3nvvmTFjxhhJ5rbbbjPvvfeeufLKK01SUpLZtGlTo8bibt26mUAgYJ5//nmzbt0606tXL2NZlunQoYP53e9+Z5577jlnmSdOnOjsV/Hx8ebOO+90bSfeY1/btm3NT37ykxq/4+h1H71N2LE7ehvwxmJvPGjXrp2R5LrQ6z32Re9n0du4PV97e62ps/dH+xhsT6MuSYu97dnHquzs7FqHtY99diy252Ov1wPFZu++5N1PEhMTY/Yle5+1O+950wknnOAck2qbbteuXV3ltm3bumJzfHy8Of/8813D2Mtjr/vi4mLnXMzuZ+8PdueNXfY8or9zb5z1nmt4Y5sUe2yvLTbbx/Y2bdo4cUbanxxEn1fY60369ljbpk0bM3Xq1APWpU+fPjHbtDc2exuR7FjsrXt0HPLGWXs929u099gYFxfnbKv2ON6kqn///jHr2Rub7e/Fnr93vz/22GNNfn7+Abc/exx7G+/atav58Y9/7HyekJBgBg4c6FpmO5Z5t1f7GGVZlpNT2J03htjHG3s9SN8eT8LhsAkGg+bCCy90jWOfc9vrLCMjw0yePNk1jL2P2sPY5zj2usrNzTXjx493jWPvB/Y49nyi12/09ih9GzPtdW9vJ/b87HL0flrburc7e/+LPofz7ju1rUf7uNW6deuY/cA+T4reLqLnk5WVFXOOM2PGDNf5yEknneSKxRdddJF59dVXXdvpVVddVWMsbtu2rZN32nE3HA67YnN9YvERmcx//fXXJhgMmhdffNHVT9p/gmSMMeeff76xLMu0b9/eXHvttebrr782lmWZ5ORkJ8letGiR6dWrlyvpDofDxhhj9u3b59rBjTFOORgMOomdfYJgB+60tDQTCATMcccd55qGZVlmz549rg3IPrDaB43CwkLXOJFIxHXiYlmWueSSS1zT8Aab6MBun5DYgaB169YmPT09ZhrRyZV9oh59RTwuLi5mnOj5FBcXu6YbHx9vQqGQU7YvOLRu3dq1LNHTs1s3vK3kw4YNc637pKSkmJM5O7m/8cYbjSTXwVmSOe+881zTiF73diIWiUTMli1bnM87dOjgOhkLh8POfO1ANHXqVNd0e/fubYqLi51y586dY+Zr123WrFnO8niHWbJkias8ePBgI8k88sgjRtp/0niwdWIvn70Mp556qmuc/Px81/IlJCQ407CD1NKlS13jtG7d2lUOBAIx8+3Xr59rmNTU1Jjl817dt7c1O2lLSUkxa9ascQ3z0EMPGUnOSY5dR/ugHgqFXONYlmWmTJlipG+Teu/25p1PIBBwyvZFBTvpte9Y8Y6TkJAQU9dHH33UVR4zZoxrHeTm5sasE/s7Tk5ONvHx8c4xqVu3bs62ZY9jXwiKbs0OBAJOXe2uT58+zjiBQMC1DQ8ZMsQkJSW5Ti6CwaBzPLX7tWnTJqau9rHP3lftutvrNXq+dl3tZNge5+KLL3ZN17u/WZbl1DU5OdmEQiGnfNJJJxnLssygQYNijsve7dF7At+xY8da9x17m4pej3Y9avv+LMty7Tt2N2DAANc4kUgkZj2OHz/erF271imHQiHXMSgzM9MYY8z27dud46W9LX322WfOulmyZIkZNGiQyc3NNZdcconzHR5//PEmPT3dqb8xxnTp0sVVPlzeWGyX7bhrjHESLzs2n3322a7YLB04Ftv7n7T/pNEbI73r1U5co2Nx9LZ38sknu6Zhb481HdfsY0dhYaFrPjW13ntjsTfRjb5A3qFDB1fMtI9j3mTLeydBdCyOj4834XDYFZvj4+NdyXAoFHLFYns/sct33323azntbbqmWGzHJfuk2HusT0pKMt///vdd+5q9Hu19av369a5xiouLY74/7106SUlJrv2iqKjINU44HHa2G3t9paWlueLo+eef7xqnttglydxzzz1Gio2zkvtYF33ssC/ceY8v3nO46O/YPt4lJCS4lq+kpMQ1jQ4dOjhlO/Hynhfl5OTE1NV77PN+X9HxLrq+0WX7PM+O2fHx8c44djJqr7esrCwTHx/vxBQ78YuLi4uJs9ENWMFg0HXMtWNZbbE5OzvbpKamusaJi4uLGadLly4HXScTJkxwDXPyySebZcuWGSk2Fntjmb3c3tiVkJBQYwyJ3g+iY+Txxx9vSkpKaozF3rpH94uOQ95YnJSUZOLj42NiV/R5RHQsji5797dWrVo543hjsd2S/5vf/MZI3+Yc3n20tnVv16mmfSc5OTnmO7bna9/p412+6PVqd979zzufjIwMY4xxxeKUlBTXOrDX/fbt251t3eaNxSkpKaZHjx6uOJmQkODUzXaosfiITOYrKiqM9G2yEd2vZ8+exhhjzjvvPOdLj0QizlWX7OxsZ6cpLi42JSUlriBsnyCuX7/etTMa8+2X36pVK1dgDgaDriuXHTp0MElJSa5pxMfHu6Zhb5zR5WAw6LpS3rp1a+cqoT2N6KutgUAgpjUr+kqrfVCMnuapp54ac4XMblGIXt7olnjvbcv2eowu9+/f3zVdbzn6YFFTZ5+sels5zzzzTNd669ChQ8w6scexd9oBAwa4pvHUU0+5ppGQkBDTkuYNoPHx8eb3v/+967vxfl8pKSkmJyfHKSclJbmuDHuDp91P+jZJy87OjrmS+LOf/cw1TXsediJbUlJiKisrnWFGjBjhWp6alu+EE04wX331lWv5/u///s9V9i5fjx49XK3Zl1xyiWu+qampMVfUe/ToYXbv3u2UvRdJpNgTVPviiH2SOHToUNd6C4fD5vrrrzeSzOTJk018fLzTGmCf+Hm3k3A4HHO1PHq7secXfeJrWZZrX0lMTHRdeEhISDAdO3Y0b731ljN+YmKi+fLLL13TjQ7K9v4TfZHHsixXOXr7t6drrxN7fw4GgzHjeG+xrenWz+hhoh8FSUlJiXlsIxwOmyFDhrj27UgkcsDHhuyLHtGfJyUlucYJBoMxJyrp6emudW1ZVsyxK3qdZGdnxyxfly5dXPNJT093XU0PBAIxd7lkZ2ebsrIyp1xcXBxzN030NmE/zhTd4piXl+eaT1JSknPB1O5KSkrM3r17nXJOTk7Mxd2CggJnW5L2nwhFb8NxcXHmyy+/NH/+859dy2OzLy4ZY8x3vvMdY1mW+d3vfmeMMaaystIEg0Fz6qmnmoEDBzrjXHHFFa7y4fLGYrt8zDHHOLHUjsXBYNB06tTJOc7YJ5zS/mP3iSeeWGMsjl5ncXFxB4yrcXFxrjhjx+Lo72bw4MGuadjjRX+f3rtN0tLSXN9VTY8QeWOx96656H3AsqyYWBwKhWJiprcF0xuLu3Tp4hwL7c4bz72xOD093TmPsL+b6H26tlhs38EYvS9Fr/uMjAznQoq0P0nwHnPLy8vNzp07XfuJN0Z679zyXhQPBAKuY0dmZmbM9xUfH++Kzd5zgLS0tJjYZR9/7O1lzJgxrnjnPa6NGDHCWT77GFJSUuKKs8OGDTtobC4uLnbNJy4uzrmwLe2Ps95W9oceesj8/e9/d9XNG2e943hjs32x8EDbW013Ldnj5OXluc5P7G0nOtGT9u/3//73v13DXH311a5yTY9lRB+HU1JSXN9Xamqq6xw1GAyadu3auZYvGAzG3FHqPccJBAIxsTk6MY+Oxd51FH2c8e7rdXn8xd6P7Zb56P06ISEh5g7U1NRUV2z2PtJrX7iLrlNGRoZrHO82HBcXF9PYkZSUFHN+Hx2LvY8xW5YVc86dnJxs3n//fdd69N4JaudQ9nfvjc3JycmuuGvfuWmX8/PzY9ZJcXGxK84GAoGYO0U6duzoGiYlJcXs27fPaTCS9p8D2PukZVmmVatWprq62tm227dv74qDdiy2x7Ebz+xYbFmW6dq1q2ucQ43FR2Qyb4wx/fv3N4MGDTKfffaZ2bt3r3nyySeNtP/k87PPPjNPP/20k1jl5eW5AoR9oLRv84hOLnr16mW++uors3z5ctcGYMy3B72CggLXTuO9mjlgwABjWZZZuXKl0y8tLc11UpGWlhZzS1NcXJx5/PHHnXJeXp5rY/a2aLZr1y5mGt66tG7d2tXvoosuirmFxtui16FDB9cwffv2dbWqe3due6eJHiZ6PvbO5j3AeZOeUCgUc5vhqaeear744gtXXe2rf9EHH3t99OrVK+ZWv+nTp7sOvH379nXV3z5oRAeCpKQkVzk9PT3mtsnc3FxXQJVk7r//ftc6+fDDD2v9/urSRQd1+2TD+4ymNxB6A1htt3tGT6Nnz54HfRYqHA67hvHeonmgizV2ZyfE0f3s4GhP23sS37NnT3PRRRc5311N6/H11193tpPk5OSYeUQiEdcBPBwOm7Zt25pnnnnG6ZednW3eeecd13jRJyrS/ivO9t0VKSkpMfub99bAYDBo2rRpY5577jnXOvjjH//ofF7TeyrsY1L0rYzRw7Ru3dp1ApGZmRlzLMjMzDQzZsxwytGB2w6K3vp6t6f4+HizatWqmO8wuuw9QUpKSnKdvNWli4uLiznh8x4fvN3IkSNd43gvOrRr1y4msUlKSjK33367U+7SpYvrZCArK8t1/OjTp48pKioy3/ve91zrx3urcHRX24lc9Hqytxv7pCguLs507NjRdVJvfw/Rt4vbyc8zzzxjQqGQc+eLnUysXLnSGPNta8FFF13kjGOMMTfffLOr3Bix2F7nHTp0MHv37nUeMQmHw06rZ/R2HP2dR697OxZ712N0XJXcsdn7XKgdi737cPQ2nJGREbPveBPZUCjkuuPL+x3XFIu9cdV7rPDG4pqO095xvLHYsqyYE29v+UDzsZfDuzyhUCgmznpvAw4GgzEXRey6DRgwwNnWovct74WV9PT0mIsv0eNEIhFXbLbfu2Jf4Krt+8rMzHTF5oULF7rieU2PF9S03x3onTyjRo1y+tUWOw8WmzMzM13xPDs7u07vAjpYrPVe3Khp+KSkJLNr1y5XP2/i6r3bZOzYsc7+VlBQELPNepMp7/Sk2IszKSkprsaxQCAQE5styzKPPfaYazr2/mZZljOf6CTTe4t2q1atYhoSkpKSnFhsT8u+a9I7H+/3Gf0+k+hptGrVyhWbO3ToEHP7f0pKipk7d67r+/IO4+3S0tLMn/70J6fsjZlS7H6clpZmXnjhBdfnNV24844TfVdLQkKCq+Gnps6++8o+5tV0fPTuc/b5bPQdAXajjb2dRB/L+vbtW+Nx+oorrnD1i46z3u3ePqfwnrNEXygIhUKmY8eOZsOGDTHD2OvOvlPLG4vtuHvOOec4n9v9osepTyw+Yt9m/+STT8oYo9atWyscDuvee+/V2LFjZVmWWrdurQsuuEDZ2dnKyclRWVmZPvzwQ0lSfHy8vvrqK0nS//73P7Vu3Vrl5eXOdL/44gtlZmZq6NChB5y/ZVm1frb/OCXt2rXL6depUyf9+9//dsq7du1SKBRyjRcIBFRSUuIaxp6WJO3bt095eXlOeePGjdq0aVOd6xU9nWhJSUmucnl5uWu+WVlZCofDrmESEhJc5fT0dO3YscPVz55Gnz59XGXbtm3bnP/j4uJUVVWljz/+2LUcL730krKzs13T3Lhxo1POzMx0pmuM0emnn67q6mrXfIYNG6b+/fs75Q0bNqiwsNApx8fHa9++fZowYYJreaLL8fHx+vTTT2PWwW233eYaZsmSJU45JSVFDzzwgFNOSkpyvr/27durJsnJycrIyHCtl0gkIkk65phjJEnZ2dnO/5I0evRo5efnO+UvvvhCBQUFTvmzzz5TXl6eqy7Z2dlatWqVU27Xrp3atWvnqssll1yiJ5980ikXFxdr2bJlTtkY49oev/76a2VmZmrBggVOv2AwqHvvvdcpf/PNN1q/fr1rPnv27HGmJ0kffPCBSktLnc/D4bCzPbz77rvKzc111ont/fff1+DBgyVJ3bp109atW9WqVSvn8927d+s3v/mNU27Tpo0ikYiKi4udfnFxcerevbtrutHzSUhIUEVFhbNO4uPjZYzR0qVLnWG2bt2qRx55xClXV1fr008/1fe+9z3XdE8//XTnf8uyVFlZ6Srv3btXkpz6hcNhvfPOO84w5eXlrv22oKAgZp8MBAK68847nXKrVq2Um5srSdq7d6+Sk5Nd85WkG264Qa+88opT3rt3r0466STXNKPnEwgEYvZrY4w6dOjglJOSklzbu2VZSkxM1AsvvOD0i4+PV+/evZ1yOBxWTk6Oa7re73zNmjXq3LmzU66oqFBcXJxT3rhxoz766CPXOJZl6bHHHnOVo4//p512musY2qpVKyUkJOhf//qXqx5vv/22ax1EfxdVVVVKSUnRc88951oH0dt0ZWWlIpGIs//s3btX69evV2JiojOMvW3NnDlTkrR06VJVVVVp8+bNuvLKK5WTkxNzvK/p+B/dzxhTpxhxKLyxOBKJKCcnRx9//LHC4bD++c9/auzYsQoGg5o2bZozXlpamj777DNJUmJiotq2batgMOh8bsdim/f79y5bTbzbprT/O50/f75T3rlzZ0ws9u5L1dXVevrpp52yN4Z6Y3F0zDpQXaOnM3ToULVp08b1eWpqqqvsjcXGmJjY/M0338TMJ3o9VFZWOtudHRej62HH4ug4GwgEXPtNdna2jDHq2LGj069169bOudXkyZNljHHF4ocfflj//Oc/dfLJJzv9EhMTXbG5vLzcVZd+/fpJkhOLe/XqpUAgoDFjxriWL/r7SkhIkDHGFZv/9Kc/6ZxzznHKlmW5Ylc0e19OTEx0xcisrCzXfDIyMpzziPT0dEn7z0ei4+zIkSMPGJu3bdum1q1bO/OpqKhQYWGha77GGE2fPt1Vxx49eriOOXFxca44W1FRoaysLKdsx+Ybb7zR6bd7924lJye7pmvHYmn//vbBBx+4Pg8EAs5x7Dvf+Y5CoZBrvywrK9Obb77plDt06KBgMKgf/vCHTr9PP/1UTzzxhFPu06ePaxr79u2TZVmu2JyYmKgf/OAHTjk9PV0VFRXO+klISFCbNm10/PHHO8Ps2rXLFZs///xzbd682bU89jmjzbIs3X777U65V69eMfuXHZfs7zExMdE1jdzcXKWkpDjllJSUmP3YeyzMyspyfRdxcXGuukv7941x48a5+kXHzGAwqPj4eKecmJio6upq3XTTTa5homOXZVkqKipylaurq9W1a1enXyAQ0He/+13XfKO3YUnO9jp69GhJ++NZ9Dn2xo0b9eWXXzrl7Oxs53h44oknOv3vuOMO5/8BAwY437EUu/9J+/f1V1991SlHIhGtXLnSKX/zzTeu/WDbtm1KT093xeL4+HitWLFCaWlpkqRQKKT169erU6dOkvZ/FwkJCXrrrbecvNDeT2qLxQeLw1I9YnGd036f2rlzp/nvf/9rjNn/Ip5TTjklpt/IkSPNxo0bnSs5I0eONNL+5/Q6d+7suk3t6quvNtu3bzf//Oc/XVfgjDm02+wTExNdtzkNHjzYNQ0p9mUqkUjEdaU7EomYzp07O+WcnBzXCzyysrJirsJHt2bVdJv9qFGjDnpbXk2dt5Uh+spvIBAw+fn5rqvup556qtPKbj9P5r0q6p1mbZ19S7q9fNEvQ4tEIs7VsgcffND86Ec/cl11syzLHH/88c4tkPYtXd51UJfOezUvLS0t5tbKA3UDBw50bstbtGiRM83PP//cNY/odRsKhZzWYfuujbi4ONcLmzp37uy6ndGyLGfbsq+8Rj/3FL3depcnuux9niwuLs4sWLDAKbdr1841X3t7ix4n+rnXQ+lqeqfCoU6jPvOpz5uQo8eJi4tznkGz31CdnJxstm3b5gyTmJjorJO8vDyTmJjotCpb//9N0fY2/dvf/tZINT8TebDOewtyenp6zG2vNa0P+/0j9rJFb5+SXK18wWAwpvXF/nWR6LJ3vpFIxNUKXZft0W6ljH6JUvT2GH0V3r4V19u6772l0Ht7ZE3bmbc10XvsyMjIcB3L0tPTTUZGhms9xsXFub4Lu4XR27IV3fJjPzf5yiuvOPPJz893jh01rbdAIGD27t3bZLfZR6sp7npj81dffWWCwaDJzMw0PXv2dGLxmWeeabKyslyPPNixOHo5D/U2+/bt28e0jnpvs48+Xtqdt+y93TY6ptrbWnQsrum79d5m743FNT0CV1NX0510tW2f9u380cMMHDjQmY/9/O+hxsOEhASTmJjoetmn3U/af1u7NxY/99xz5owzznDFzISEhEOKofZ37H3JaF1+DSj6/CM6Ftut4fY6sFsHvftsdCyW9rdC27G6tuN0fn5+rbG5tn24ppi5cOFCZ1uzWxGjt0f7ud/oaUS39NqxObo12Dvfunb1icUNMc6h1tW7HhMTE2Pu4Iheb5ZlmfT0dPPyyy+7thk7ZtjvkbDjX/SL8Q5WN+/2WdO7hA60Tdjf9//+9z9Xv+hpWJbl2j7T09NNMBh03a5f0ztGatoevXcgHGic6HMA+23u0dtmIBCo8dc87O3TfulpXb5fbyz2vqg7NTXVdU6QnJzs2v/suyCjY3N8fLzp379/zPE6evsLhULGmG9jcVpamjHGxMRib/y2Y3FD3GZ/xLbM25KSkpSfn69t27bp1Vdf1bhx42L6nX322c5Vry+++EJnn322pP1X5NevX+9ceYuPj9dLL72ktLQ0HXfccc48vFeDvvrqKw0YMEDS/qs21dXVztXXtLQ0bdy4UZZlua7Oea/kS3Jar+yrMykpKa4WrD179ujrr792yuXl5Tr22GMl7W9ZLC8vd7U+SnK1MLRt21Y7duxwrjAVFBRo6dKlrnrFx8e7xrGvptlXxKX9V7uirzxG11mSOnbsqK1btzr9CgsLtXLlSu3evVuSnPXrbf2IvnPhrrvuUiAQcIax12dycrKrNbiiosK5ytumTRvt3r3bNd158+a55pGYmKh169Y5Vznbt2/v+i7slpDExEStWLHCtV6iy+Fw2FlvgcD+3cp79TQYDDrjhEIhBQIBV0vhv//9b2e7sVmW5WrF2bt3r7Nd2C0k9lVS+2qgZVn64x//6AyzceNG1/ZoolpG7b/2VUdbUlKSVq9e7Sp7W4+9270xxrV+//vf/7qWZ9++fa6WRS+71c2++8RejyNHjpT0bctbq1atNHnyZNe4Dz74oCQ5rTr2/ml/J/Hx8Vq8eLGzDtLT0zVlyhRJcuoUvQ6CwaBGjx6tv/3tb06//Px81zqRpFNOOcVV10gkooEDBzqfjxgxQhdddJFTTk9Pd7avoqIi7du3TxkZGU7LjSTXlf4vvvhCX3/9tbp16+bUobKy0hnebtnyfhfS/pYRW1xcnMaPH+/63Hts2LFjh2s7CQaDMXfl3HTTTa5WkmAwGNPKOHz4cEn7t/Hq6mrXnSTS/u3U3icDgYAqKyud+drLnp6e7tpvExISXOvesixne7TXvd0iYrfYWJbl2h53797ttLZ17NjR1eJuT8MYo759+zr9//vf/zqtGW3btlVycrKrRSgSiah169aucaqqqlzl8vJy1/Fwx44dSktLc7VyGU8L8b59+5SWlqYVK1aoe/fuio+PV0FBgf7zn/84w9jLe+KJJzp3EOTk5GjYsGH661//KkkaNWqUVq9ereOOO05ZWVk655xzFAwGFQqF1LdvX61cudK1jy5ZsiTmGNRQaoq73tickZGhY489Vtu2bdNHH33kxOJNmzapoqLCWY/RsVj6Ns62b9/eFSOjj6/S/uOn/f2lpqZqw4YNzv6fnp4uy7K0b98+1zSij5c2O85G77djx451/o++C83e1qL3t6qqKlcLtCRX3C0qKnLFYjtm2vO1efe96Fhsb9Peu4mijxWdOnVyxWJpfxyy52Mfq6K33x49esiyLKfV3V7Hbdu2deb7zTffKBwOO9uhtL8VbOLEic76id438/LyNGvWLFcslvYf3+xydCy2BQIBpaamOnE1Li5OKSkpznyjY7H07Z0MCQkJMfE7+q6H6Fh8zDHHyBjjHAuj46zNG4vj4uL00UcfOdtnYmKiLMty6mP7/PPPa43N9rlW27ZtnWNfIBCI+T6lb8+HOnbsqOrqalVXV7u2x+g7WqT921/0nU12bL7gggtc68Ser72d2/ujzV4++/y5ffv2Tsz0xuKkpCQFg0GnJdT+Lnr37u2Ks1lZWa67IuLj453YZa+/gQMHusZJTk526pqSkiLLslyxKzk5WV26dHG1wnrj3zfffBNzN0bPnj2d/40xKikpccW/zZs3O+uxXbt2+vrrr51twD5vSUtLc8UubyyOj493Ypm9v3vvMoq+S0vaH1vtaSYkJGjv3r1KSkpyfafRrfD2HXJ23UKhkLZv365wOKwXX3zRGc57V5307TmiHY9btWrlikPeuzekb4+PCQkJqq6udo4j0fuuHSM7duyoHTt2uJa5qqrKma49jjHGuYu3bdu2iouLcx3/7FgcXfdgMOi6OyESibiOO19//bUr3u3du1fx8fGu2Lx3716FQiGtWLFCGRkZCgaDmjJlip5//nlnmBEjRkj69i4C+7zMG4v/85//KCkpSSUlJVq9erUTi8PhcMwdHocci+uc9vvMK6+8Yv785z+bjz/+2CxevNh5VvpPf/qTeeedd8zZZ59tCgoKTPv27c2CBQvMMccc41xpsZ99s68a23/tZ7Xy8/OdN2Hb3bhx41xl+6UZ9jTtqzr2lelwOGzuu+++A07De1VJkuvZzJo6+4q6/fxsTb/h6+3s9wXYV768z7/X1HmflaqprnbnbfnwPmdcl5Z/+zvwXnGXYl/C4v1ZGvslV3Y9arrS712v9ls/7XG6dOliHn74Ydcw3p+v8D4z2LZtW/OLX/zCtR68P0dld/Z2Yq+L6CuXduuU3Xl/msduUYi+Umi/CdTuvD/p4S0XFRXFvCnZfvnRgZbP+5Nc3s77szutWrUyV1111QHr4v1JIO+V4VAoFNOieuaZZ7q+L+9PLUn7340QPY69D9vDeLeL3r17m5/+9Keuft6f7/NuJ5L7BYVZWVkxrb3en79p3bp1zE8Uel+C5P35G/uOnOiWLe/xw7vuvS+Yia7/gYaJ7lq3bm3mzJnj6ud9QaN9p4x9PKnpp+m849gvjIt+2Y33+7J/Js87jt15f/6zps5+TtI7TPR24l339h1U9t0QNbUQeL+f6LuFauoyMjKcX9ewO+9x7LTTTjMLFy50jsedO3eOiRnXXnuteeWVV5ztYNSoUeb55593jgk33HCDMcaYQYMGmaysLBMIBMy8efNcP013xx13mPfee89MmzbNJCUlmY0bNzZqLG7fvr0pKSkxa9euNX/4wx+cO9l+85vfmKefftpZ3oyMDPPggw86yxr9Qjz7+7Nb6qPvMPK2LnpfvuSNVXb8i245j37rek37if0uiejjtPenlryd98VtB3sGVvr2l2bsFqcDxVnv9lvbi+qiO++zq9HHJOnAz457Y4x3my8uLo7Zxr371tlnn+2qR3Fxccwzyd7Y7H3rdbdu3Vyxubi42Fx33XU1bgP2fnLMMceYefPmHXA92sdPe53UtC68+2xtP595oO2kttgcfU5w8803u6bpjdX29hk9H/tnHWubT/TLCKX9+4533Xt/6sy7vN5zZEkxPynq/clU+xzHPo6GQiEzbdo01zjeY7v3HSQ1xWZ7vdbWyh8Oh2N+PtL7EjnvfDt06OA67ykpKYk51tf284vR9fCeS3m3Ye85nRT7s8Xezn6Bb/RPwB7sO7fvwIn+GbbobUuS+e53v1vjdmLPJzc31/XuJ+nb/dju7HMc7/El+g4ge5+011NtP7sZHW/tn1e08xvvSyrtYaPH8f6Mo7fzxpDk5OQaf2Eiut8111zjer/LwIEDzSuvvOL6uc7p06eb559/3nmXlP1LHXYcOO+885y4a/80nR2b6xOLj9hk/tlnnzUdOnQwoVDI5OXlmcsuu8w8/vjjTr9QKOQ68EUikZiDGx0dHR0dXUvrkpOTTW5urusiUmpqqlNOTU01rVq1ciXzKSkp5pRTTjFFRUUmFAqZY4891kybNs1VXrFiRaPH4pEjR5p27dqZUChkcnNzTWFhoeviSiQSOeBvsNPR0dHR0bWELvqFlJFIxKSmppqioiInFluW5Xrh3aBBg0w4HDZpaWmuuPvrX//6sGKxZUwNb4ABAAAAAAAt1hH/zDwAAAAAAEcaknkAAAAAAHyGZB4AAAAAAJ8hmQcAAAAAwGdI5gEAAAAA8BmSeQAAAAAAfIZkHgAAAAAAnyGZBwAAAADAZ0jmgQa0fPlyWZal7du3N3dVGt38+fOVnp7ulOfMmaPevXu7hpkzZ45yc3NlWZZ+//vfa/LkyTrjjDMavW72/JrCwIED9fTTTzfJvGoyePBgTZs2rc7Dv/jii+rTp4/27dvXeJUCgGZELO7tGoZY3PiIxWguJPM4Ik2ePFmWZcmyLMXHx6tDhw6aMWOGdu3a1ajzHTBggLZs2aK0tLRGnU9LNGPGDP3lL39xyu+//75uvPFGPfzww9qyZYvGjBmjX/3qV5o/f36DzbOmkxZJzvwa24svvqiysjJNmDCh0efVUE477TRZltWsJz0Ajg7E4qZHLPYHYjEaCsk8jlijR4/Wli1b9PHHH+umm27SAw88oBkzZtQ47J49expknqFQSHl5ebIsq0Gm5yfJycnKyspyyuvXr5ckjRs3Tnl5eQqHw0pLS3O1IDQWe36N7d5779UPfvADBQKHdyhtqO2vrn7wgx/ovvvua9J5Ajg6EYubFrG4/ojF8COSeRyxwuGw8vLyVFhYqPPOO0/nn3++c7uXfRX5scceU4cOHRQOh2WMUXl5uS666CLl5OQoNTVVQ4cO1X/+8x9J0tq1a2VZlj744APXfO6++261a9dOxpgab+174YUX1K1bN4XDYbVr10533XWXa/yabkNLT093rppXVVVp6tSpys/PV0JCgtq1a6dbb7211uWurq7W1VdfrfT0dGVlZWnmzJmaNGmS65a6du3a6Z577nGN17t3b82ZM8e1XD169FBSUpIKCwt16aWXaufOnbXON/rK/Jw5c3T66adLkgKBgHNC5b21b9++fbr99tvVqVMnhcNhtW3bVjfffLPz+axZs1RSUqLExER16NBB1113nRNs58+frxtvvFH/+c9/nJYfe5151+k777yjoUOHKhKJKCsrSxdddJFrWex6/fKXv1R+fr6ysrJ02WWXHTCwf/HFF1q6dKnGjh3r6v/BBx/o5JNPVkJCgrp27aqlS5e66rNx40ZZlqXnnntOgwcPVkJCgp566il9+eWX+t73vqc2bdooMTFRPXr00DPPPOOa9q5duzRx4kQlJycrPz8/ZluS9m8vM2fOVOvWrZWUlKQTTjhBy5cvdw0zduxY/fOf/9THH39c6/IBQEMgFhOLbcTi5a5hiMVoCCTzOGpEIhFXQPjoo4/03HPP6YUXXtDq1aslSaeeeqrKysr08ssvq7S0VMcee6yGDRumr776Sp07d1bfvn3129/+1jXdp59+Wuedd16NLQClpaUaP368JkyYoHfeeUdz5szRddddd0i3t91777364x//qOeee05r167VU089pXbt2tU6/F133aXHHntM8+bN0+uvv66vvvpKixYtqvP8bIFAQPfee6/WrFmjJ554Qn/96181c+bMOo07Y8YMPf7445L232a3ZcuWGoebPXu2br/9dl133XV677339PTTTys3N9f5PCUlRfPnz9d7772nX/3qV3r00Uc1d+5cSdK5556r6dOnq1u3bs48zj333Jh5fP311xo9erQyMjL05ptv6v/+7/+0dOlSTZ061TXcsmXLtH79ei1btkxPPPGE5s+ff8Dv6fXXX1diYqKOOeYYp9++fft0xhlnKDExUf/4xz/0yCOP6Nprr61x/FmzZumKK67Q+++/r1GjRumbb75R37599eKLL2rNmjW66KKLdMEFF+gf//iHM84111yjZcuWadGiRVq8eLGWL1+u0tJS13R/8IMfaOXKlVq4cKHefvttnXPOORo9erTWrVvnDFNUVKScnBy99tprtS4fADQGYvGhIRYTi4EDMsARaNKkSWbcuHFO+R//+IfJysoy48ePN8YYc8MNN5j4+HizdetWZ5i//OUvJjU11XzzzTeuaXXs2NE8/PDDxhhj7r77btOhQwfns7Vr1xpJ5t133zXGGLNs2TIjyWzbts0YY8x5551nRowY4ZreNddcY7p27eqUJZlFixa5hklLSzOPP/64McaYyy+/3AwdOtTs27evTsuen59vbrvtNqe8Z88e06ZNG9f6KCoqMnPnznWN16tXL3PDDTfUOt3nnnvOZGVlOeXHH3/cpKWlOeUbbrjB9OrVyykvWrTIeA8x0d9LRUWFCYfD5tFHH63TchljzB133GH69u1b6zxt0ev0kUceMRkZGWbnzp3O5y+99JIJBAKmrKzMqVdRUZHZu3evM8w555xjzj333FrrMnfuXNe2YIwxf/7zn01cXJzZsmWL02/JkiWu+mzYsMFIMvfcc89Bl/eUU04x06dPN8YYs2PHDhMKhczChQudz7/88ksTiUTMlVdeaYwx5qOPPjKWZZnPPvvMNZ1hw4aZ2bNnu/r16dPHzJkz56B1AID6IhYTi4nF3yIWozHQMo8j1osvvqjk5GQlJCSof//+GjhwoOvZpKKiIrVq1copl5aWaufOncrKylJycrLTbdiwwXnmbMKECdq0aZPeeOMNSdJvf/tb9e7dW127dq2xDu+//75OOukkV7+TTjpJ69atU3V1dZ2WY/LkyVq9erU6d+6sK664QosXL6512PLycm3ZskX9+/d3+sXFxem4446r07yiLVu2TCNGjFDr1q2VkpKiiRMn6ssvv2ywFxe9//77qqys1LBhw2od5vnnn9fJJ5+svLw8JScn67rrrtMnn3xyyPPp1auXkpKSnH4nnXSS9u3bp7Vr1zr9unXrpmAw6JTz8/O1devWWqe7e/duJSQkuPqtXbtWhYWFysvLc/odf/zxNY7v/U6qq6t18803q2fPns42uHjxYmd5169fr6qqKtd3m5mZqc6dOzvlf/3rXzLGqKSkxLUNr1ixwtmGbZFIRF9//XWtywcADYFYvB+xmFhMLEZjiGvuCgCNZciQIXrwwQcVHx+vgoICxcfHuz6PDijS/tuy8vPzY55pkuS8KCY/P19DhgzR008/rRNPPFHPPPOMLr744lrrYIyJueXPGOMqW5YV0y/6FsRjjz1WGzZs0J///GctXbpU48eP1/Dhw/X888/XOt+DCQQCB5znpk2bdMopp+iSSy7RL37xC2VmZur111/XlClTGuwFMZFI5ICfv/HGG5owYYJuvPFGjRo1SmlpaVq4cGGNz6YdSE3fgS26v3f7sCzrgD8Zk52drW3bttV5Xl7e7e+uu+7S3Llzdc899zjPR06bNk1VVVXOtA9m3759CgaDKi0tdZ0MSftfihTtq6++cp1AA0BjIBbXjli8H7GYWIz6o2UeR6ykpCR16tRJRUVFMcGhJscee6zKysoUFxenTp06ubrs7GxnuPPPP1/PPvus/v73v2v9+vUH/CmUrl276vXXX3f1W7VqlUpKSpwDfKtWrVzPsa1bty7mKm1qaqrOPfdcPfroo3r22Wf1wgsv6KuvvoqZX1pamvLz853WCknau3dvzLNc3nlWVFRow4YNTvmtt97S3r17ddddd+nEE09USUmJ/vvf/9a6nPVRXFysSCTi+gmdaCtXrlRRUZGuvfZaHXfccSouLtamTZtcw4RCoYO2qnTt2lWrV692tWKsXLlSgUBAJSUl9a5/nz59VFZW5jqJ6NKliz755BP973//c/q9+eabdZrea6+9pnHjxun73/++evXqpQ4dOrierevUqZPi4+Nd3+22bdv04YcfuupUXV2trVu3xmzD0S0U33zzjdavX68+ffrUa9kBoK6IxfsRi4nFxGI0BpJ54P8bPny4+vfvrzPOOEOvvvqqNm7cqFWrVulnP/uZ3nrrLWe4s846SxUVFfrxj3+sIUOGqHXr1rVOc/r06frLX/6iX/ziF/rwww/1xBNP6P7773f9LM/QoUN1//3361//+pfeeustXXLJJa4Tnrlz52rhwoX64IMP9OGHH+r//u//lJeXV+vPylx55ZW67bbbtGjRIn3wwQe69NJLXW/0tef55JNP6rXXXtOaNWs0adIk19Xjjh07au/evbrvvvv08ccf68knn9RDDz10iGv0wBISEjRr1izNnDlTCxYs0Pr16/XGG29o3rx5kvYHzE8++UQLFy7U+vXrde+998a8PKhdu3basGGDVq9erS+++EKVlZUx8zn//POVkJCgSZMmac2aNVq2bJkuv/xyXXDBBa4X/ByqPn36qFWrVlq5cqXTb8SIEerYsaMmTZqkt99+WytXrnReunOwVoJOnTppyZIlWrVqld5//31dfPHFKisrcz5PTk7WlClTdM011+gvf/mL1qxZo8mTJ7t+iqekpETnn3++Jk6cqN/97nfasGGD3nzzTd1+++16+eWXneHeeOMNhcNh122CANASEIuJxYeCWIyjHck88P9ZlqWXX35ZAwcO1IUXXqiSkhJNmDBBGzdudAWa1NRUnX766frPf/6j888//4DTPPbYY/Xcc89p4cKF6t69u66//nr9/Oc/1+TJk51h7rrrLhUWFmrgwIE677zzNGPGDCUmJjqfJycn6/bbb9dxxx2nfv36aePGjXr55Zdr/T3V6dOna+LEiZo8ebL69++vlJQUnXnmma5hZs+erYEDB+q0007TKaecojPOOEMdO3Z0Pu/du7fuvvtu3X777erevbt++9vfHvAneOrruuuu0/Tp03X99dfrmGOO0bnnnus8Gzdu3DhdddVVmjp1qnr37q1Vq1bpuuuuc41/9tlna/To0RoyZIhatWoV8/MxkpSYmKhXX31VX331lfr166fvfve7GjZsmO6///7DqnswGNSFF17oeqNyMBjU73//e+3cuVP9+vXTD3/4Q/3sZz+TpJhn+mpaF8cee6xGjRqlwYMHKy8vz/XTQZJ05513auDAgRo7dqyGDx+uk08+WX379nUN8/jjj2vixImaPn26OnfurLFjx+of//iHCgsLnWGeeeYZnX/++a7tDABaAmIxsfhQEItxtLNMXR7+AOBrkydP1vbt22N+QxeH53//+5+6deum0tJSFRUV1TjMypUrdfLJJ+ujjz5ynaQ1l88//1xdunTRW2+9pfbt2zd3dQDgqEEsbhzEYhzNeAEeANRTbm6u5s2bp08++cQ5gVi0aJGSk5NVXFysjz76SFdeeaVOOumkFnHyIEkbNmzQAw88wMkDAOCIQCzG0YxkHgAOw7hx41zlHTt2aObMmdq8ebOys7M1fPjwQ37rb2M6/vjja/2JHgAA/IhYjKMVt9kDAAAAAOAzvAAPAAAAAACfIZkHAAAAAMBnSOYBAAAAAPAZknkAAAAAAHyGZB4AAAAAAJ8hmQcAAAAAwGdI5gEAAAAA8BmSeQAAAAAAfIZkHgAAAAAAn/l/QE7HgOsagDYAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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gAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwgEIN8JMmTdJdd92lgIAABQcHq3v37jpw4IDTmD59+shmszldmjVr5jQmMzNTQ4YMUVBQkPz9/dW1a1edOHHCaUxqaqqioqJkt9tlt9sVFRWl06dPF/QmAgAAAADgEYUa4OPj4zVo0CBt3rxZcXFx+uuvv9S+fXudPXvWaVzHjh2VlJTkuKxcudLp9piYGC1dulSLFy/Wxo0blZGRoc6dOys7O9sxpnfv3kpISFBsbKxiY2OVkJCgqKiom7KdAAAAAAC4q0Rhrjw2Ntbp+ty5cxUcHKwdO3bo/vvvdyz39fVVaGhonjXS0tI0Z84cLViwQG3btpUkLVy4UJUqVdKaNWvUoUMH7d+/X7Gxsdq8ebOaNm0qSfrwww8VGRmpAwcOqFatWgW0hQAAAAAAeEaR+gx8WlqaJCkwMNBp+bp16xQcHKyaNWuqf//+SklJcdy2Y8cOXbx4Ue3bt3csCw8PV/369bVp0yZJ0g8//CC73e4I75LUrFkz2e12x5grZWZmKj093ekCAADyR/8EAKDgFJkAb4zRiBEjdO+996p+/fqO5Z06ddKiRYu0du1aTZkyRdu2bVPr1q2VmZkpSUpOTpaPj4/Kli3rVC8kJETJycmOMcHBwbnWGRwc7BhzpUmTJjk+L2+321WpUiVPbSoAAP9Y9E8AAApOkQnwgwcP1u7du/Xpp586Le/Vq5cefPBB1a9fX126dNE333yjgwcPasWKFfnWM8bIZrM5rl/+89XGXG7cuHFKS0tzXI4fP+7CVgEAULzQPwEAKDiF+hn4S4YMGaLly5dr/fr1qlixYr5jw8LCFBERoUOHDkmSQkNDlZWVpdTUVKej8CkpKWrevLljzO+//56r1h9//KGQkJA81+Pr6ytfX19XNwkAgGKJ/gkAQMEp1CPwxhgNHjxYX3zxhdauXauqVate8z4nT57U8ePHFRYWJklq3LixvL29FRcX5xiTlJSkvXv3OgJ8ZGSk0tLStHXrVseYLVu2KC0tzTEGAAAAAICirFCPwA8aNEiffPKJvvzySwUEBDg+j2632+Xn56eMjAxNmDBBDz/8sMLCwnTkyBE999xzCgoKUo8ePRxj+/Xrp5EjR6pcuXIKDAzUqFGj1KBBA8dZ6evUqaOOHTuqf//+mjVrliTpqaeeUufOnTkDPQAAAADAEgo1wM+cOVOS1LJlS6flc+fOVZ8+feTl5aU9e/Zo/vz5On36tMLCwtSqVSstWbJEAQEBjvHTpk1TiRIl1LNnT50/f15t2rTRvHnz5OXl5RizaNEiDR061HG2+q5du2rGjBkFv5EAAAAAAHiAzRhjCnsSVpCeni673a60tDS1emWZ2/V2vPmk+5MCAKCIu7x/lilTprCnAwCApRWZs9ADAAAAAICrI8ADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgo1wE+aNEl33XWXAgICFBwcrO7du+vAgQNOY4wxmjBhgsLDw+Xn56eWLVtq3759TmMyMzM1ZMgQBQUFyd/fX127dtWJEyecxqSmpioqKkp2u112u11RUVE6ffp0QW8iAAAAAAAeUagBPj4+XoMGDdLmzZsVFxenv/76S+3bt9fZs2cdYyZPnqypU6dqxowZ2rZtm0JDQ9WuXTudOXPGMSYmJkZLly7V4sWLtXHjRmVkZKhz587Kzs52jOndu7cSEhIUGxur2NhYJSQkKCoq6qZuLwAAAAAArrIZY0xhT+KSP/74Q8HBwYqPj9f9998vY4zCw8MVExOjsWPHSvr7aHtISIjeeOMNPf3000pLS1P58uW1YMEC9erVS5L022+/qVKlSlq5cqU6dOig/fv3q27dutq8ebOaNm0qSdq8ebMiIyP1008/qVatWtecW3p6uux2u9LS0tTqlWVub+uON590uwYAAEXd5f2zTJkyhT0dAAAsrUh9Bj4tLU2SFBgYKElKTExUcnKy2rdv7xjj6+urFi1aaNOmTZKkHTt26OLFi05jwsPDVb9+fceYH374QXa73RHeJalZs2ay2+2OMQAAAAAAFGUlCnsClxhjNGLECN17772qX7++JCk5OVmSFBIS4jQ2JCRER48edYzx8fFR2bJlc425dP/k5GQFBwfnWmdwcLBjzJUyMzOVmZnpuJ6enu7ilgEAUHzQPwEAKDhF5gj84MGDtXv3bn366ae5brPZbE7XjTG5ll3pyjF5jc+vzqRJkxwnvLPb7apUqdL1bAYAAMUa/RMAgIJTJAL8kCFDtHz5cn333XeqWLGiY3loaKgk5TpKnpKS4jgqHxoaqqysLKWmpuY75vfff8+13j/++CPX0f1Lxo0bp7S0NMfl+PHjrm8gAADFBP0TAICCU6gB3hijwYMH64svvtDatWtVtWpVp9urVq2q0NBQxcXFOZZlZWUpPj5ezZs3lyQ1btxY3t7eTmOSkpK0d+9ex5jIyEilpaVp69atjjFbtmxRWlqaY8yVfH19VaZMGacLAADIH/0TAICCU6ifgR80aJA++eQTffnllwoICHAcabfb7fLz85PNZlNMTIwmTpyoGjVqqEaNGpo4caJKlSql3r17O8b269dPI0eOVLly5RQYGKhRo0apQYMGatu2rSSpTp066tixo/r3769Zs2ZJkp566il17tz5us5ADwAAAABAYSvUAD9z5kxJUsuWLZ2Wz507V3369JEkjRkzRufPn9fAgQOVmpqqpk2bavXq1QoICHCMnzZtmkqUKKGePXvq/PnzatOmjebNmycvLy/HmEWLFmno0KGOs9V37dpVM2bMKNgNBAAAAADAQ4rU98AXZXwPPAAAN47vgQcAwHOKxEnsAAAAAABA/gjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFlCoAX79+vXq0qWLwsPDZbPZtGzZMqfb+/TpI5vN5nRp1qyZ05jMzEwNGTJEQUFB8vf3V9euXXXixAmnMampqYqKipLdbpfdbldUVJROnz5dwFsHAAAAAIDnFGqAP3v2rBo1aqQZM2ZcdUzHjh2VlJTkuKxcudLp9piYGC1dulSLFy/Wxo0blZGRoc6dOys7O9sxpnfv3kpISFBsbKxiY2OVkJCgqKioAtsuAAAAAAA8rURhrrxTp07q1KlTvmN8fX0VGhqa521paWmaM2eOFixYoLZt20qSFi5cqEqVKmnNmjXq0KGD9u/fr9jYWG3evFlNmzaVJH344YeKjIzUgQMHVKtWLc9uFAAAAAAABaDIfwZ+3bp1Cg4OVs2aNdW/f3+lpKQ4btuxY4cuXryo9u3bO5aFh4erfv362rRpkyTphx9+kN1ud4R3SWrWrJnsdrtjTF4yMzOVnp7udAEAAPmjfwIAUHCKdIDv1KmTFi1apLVr12rKlCnatm2bWrdurczMTElScnKyfHx8VLZsWaf7hYSEKDk52TEmODg4V+3g4GDHmLxMmjTJ8Zl5u92uSpUqeXDLAAD4Z6J/AgBQcIp0gO/Vq5cefPBB1a9fX126dNE333yjgwcPasWKFfnezxgjm83muH75z1cbc6Vx48YpLS3NcTl+/LjrGwIAQDFB/wQAoOAU6mfgb1RYWJgiIiJ06NAhSVJoaKiysrKUmprqdBQ+JSVFzZs3d4z5/fffc9X6448/FBISctV1+fr6ytfX18NbAADAPxv9EwCAguPSEfjWrVvn+TVs6enpat26tbtzuqqTJ0/q+PHjCgsLkyQ1btxY3t7eiouLc4xJSkrS3r17HQE+MjJSaWlp2rp1q2PMli1blJaW5hgDAAAAAEBR59IR+HXr1ikrKyvX8gsXLmjDhg3XXScjI0OHDx92XE9MTFRCQoICAwMVGBioCRMm6OGHH1ZYWJiOHDmi5557TkFBQerRo4ckyW63q1+/fho5cqTKlSunwMBAjRo1Sg0aNHCclb5OnTrq2LGj+vfvr1mzZkmSnnrqKXXu3Jkz0AMAAAAALOOGAvzu3bsdP//4449OJ4HLzs5WbGysKlSocN31tm/frlatWjmujxgxQpIUHR2tmTNnas+ePZo/f75Onz6tsLAwtWrVSkuWLFFAQIDjPtOmTVOJEiXUs2dPnT9/Xm3atNG8efPk5eXlGLNo0SINHTrUcbb6rl275vvd8wAAAAAAFDU2Y4y53sG33HKL48Rved3Nz89P7777rvr27eu5GRYR6enpstvtSktLU6tXlrldb8ebT7o/KQAAirjL+2eZMmUKezoAAFjaDR2BT0xMlDFG1apV09atW1W+fHnHbT4+PgoODnY68g0AAAAAADzjhgJ8RESEJCknJ6dAJgMAAAAAAPLm8tfIHTx4UOvWrVNKSkquQP/SSy+5PTEAAAAAAPAfLgX4Dz/8UM8884yCgoIUGhrq+Fy8JNlsNgI8AAAAAAAe5lKAf/XVV/Xaa69p7Nixnp4PAAAAAADIwy2u3Ck1NVWPPPKIp+cCAAAAAACuwqUA/8gjj2j16tWengsAAAAAALgKl95CX716db344ovavHmzGjRoIG9vb6fbhw4d6pHJAQAAAACAv9mMMeZG71S1atWrF7TZ9Msvv7g1qaIoPT1ddrtdaWlpavXKMrfr7XjzSfcnBQBAEXd5/yxTpkxhTwcAAEtz6Qh8YmKip+cBAAAAAADy4dJn4AEAAAAAwM3l0hH4vn375nv7Rx995NJkAAAAAABA3lwK8KmpqU7XL168qL179+r06dNq3bq1RyYGAAAAAAD+w6UAv3Tp0lzLcnJyNHDgQFWrVs3tSQEAAAAAAGce+wz8LbfcouHDh2vatGmeKgkAAAAAAP4fj57E7ueff9Zff/3lyZIAAAAAAEAuvoV+xIgRTteNMUpKStKKFSsUHR3tkYkBAAAAAID/cCnA79q1y+n6LbfcovLly2vKlCnXPEM9AAAAAAC4cS4F+O+++87T8wAAAAAAAPlwKcBf8scff+jAgQOy2WyqWbOmypcv76l5AQAAAACAy7h0EruzZ8+qb9++CgsL0/3336/77rtP4eHh6tevn86dO+fpOQIAAAAAUOy5FOBHjBih+Ph4ffXVVzp9+rROnz6tL7/8UvHx8Ro5cqSn5wgAAAAAQLHn0lvoP//8c/3v//6vWrZs6Vj2wAMPyM/PTz179tTMmTM9NT8AAAAAACAXj8CfO3dOISEhuZYHBwfzFnoAAAAAAAqASwE+MjJS48eP14ULFxzLzp8/r3//+9+KjIz02OQAAAAAAMDfXHoL/fTp09WpUydVrFhRjRo1ks1mU0JCgnx9fbV69WpPzxEAAAAAgGLPpQDfoEEDHTp0SAsXLtRPP/0kY4weffRRPf744/Lz8/P0HAEAAAAAKPZcCvCTJk1SSEiI+vfv77T8o48+0h9//KGxY8d6ZHIAAAAAAOBvLn0GftasWapdu3au5fXq1dMHH3zg9qQAAAAAAIAzlwJ8cnKywsLCci0vX768kpKS3J4UAAAAAABw5lKAr1Spkr7//vtcy7///nuFh4e7PSkAAAAAAODMpc/A/+tf/1JMTIwuXryo1q1bS5K+/fZbjRkzRiNHjvToBAEAAAAAgIsBfsyYMTp16pQGDhyorKwsSVLJkiU1duxYjRs3zqMTBAAA/wyNR893u8aON5/0wEwAALAmlwK8zWbTG2+8oRdffFH79++Xn5+fatSoIV9fX0/PDwAAAAAAyMUAf0np0qV11113eWouAAAAAADgKlw6iR0AAAAAALi5CPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAGFGuDXr1+vLl26KDw8XDabTcuWLXO63RijCRMmKDw8XH5+fmrZsqX27dvnNCYzM1NDhgxRUFCQ/P391bVrV504ccJpTGpqqqKiomS322W32xUVFaXTp08X8NYBAAAAAOA5hRrgz549q0aNGmnGjBl53j558mRNnTpVM2bM0LZt2xQaGqp27drpzJkzjjExMTFaunSpFi9erI0bNyojI0OdO3dWdna2Y0zv3r2VkJCg2NhYxcbGKiEhQVFRUQW+fQAAAAAAeEqJwlx5p06d1KlTpzxvM8Zo+vTpev755/XQQw9Jkj7++GOFhITok08+0dNPP620tDTNmTNHCxYsUNu2bSVJCxcuVKVKlbRmzRp16NBB+/fvV2xsrDZv3qymTZtKkj788ENFRkbqwIEDqlWr1s3ZWAAAAAAA3FBkPwOfmJio5ORktW/f3rHM19dXLVq00KZNmyRJO3bs0MWLF53GhIeHq379+o4xP/zwg+x2uyO8S1KzZs1kt9sdYwAAAAAAKOoK9Qh8fpKTkyVJISEhTstDQkJ09OhRxxgfHx+VLVs215hL909OTlZwcHCu+sHBwY4xecnMzFRmZqbjenp6umsbAgBAMUL/BACg4BTZI/CX2Gw2p+vGmFzLrnTlmLzGX6vOpEmTHCe9s9vtqlSp0g3OHACA4of+CQBAwSmyAT40NFSSch0lT0lJcRyVDw0NVVZWllJTU/Md8/vvv+eq/8cff+Q6un+5cePGKS0tzXE5fvy4W9sDAEBxQP8EAKDgFNkAX7VqVYWGhiouLs6xLCsrS/Hx8WrevLkkqXHjxvL29nYak5SUpL179zrGREZGKi0tTVu3bnWM2bJli9LS0hxj8uLr66syZco4XQAAQP7onwAAFJxC/Qx8RkaGDh8+7LiemJiohIQEBQYGqnLlyoqJidHEiRNVo0YN1ahRQxMnTlSpUqXUu3dvSZLdble/fv00cuRIlStXToGBgRo1apQaNGjgOCt9nTp11LFjR/Xv31+zZs2SJD311FPq3LkzZ6AHAAAAAFhGoQb47du3q1WrVo7rI0aMkCRFR0dr3rx5GjNmjM6fP6+BAwcqNTVVTZs21erVqxUQEOC4z7Rp01SiRAn17NlT58+fV5s2bTRv3jx5eXk5xixatEhDhw51nK2+a9euV/3ueQAAAAAAiiKbMcYU9iSsID09XXa7XWlpaWr1yjK36+1480n3JwUAQBFH/wQAwHOK7GfgAQAAAADAfxDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALKBIB/gJEybIZrM5XUJDQx23G2M0YcIEhYeHy8/PTy1bttS+ffucamRmZmrIkCEKCgqSv7+/unbtqhMnTtzsTQEAAAAAwC1FOsBLUr169ZSUlOS47Nmzx3Hb5MmTNXXqVM2YMUPbtm1TaGio2rVrpzNnzjjGxMTEaOnSpVq8eLE2btyojIwMde7cWdnZ2YWxOQAAAAAAuKREYU/gWkqUKOF01P0SY4ymT5+u559/Xg899JAk6eOPP1ZISIg++eQTPf3000pLS9OcOXO0YMECtW3bVpK0cOFCVapUSWvWrFGHDh1u6rYAAAAAAOCqIn8E/tChQwoPD1fVqlX16KOP6pdffpEkJSYmKjk5We3bt3eM9fX1VYsWLbRp0yZJ0o4dO3Tx4kWnMeHh4apfv75jzNVkZmYqPT3d6QIAAPJH/wQAoOAU6QDftGlTzZ8/X6tWrdKHH36o5ORkNW/eXCdPnlRycrIkKSQkxOk+ISEhjtuSk5Pl4+OjsmXLXnXM1UyaNEl2u91xqVSpkge3DACAfyb6JwAABadIB/hOnTrp4YcfVoMGDdS2bVutWLFC0t9vlb/EZrM53ccYk2vZla5nzLhx45SWlua4HD9+3MWtAACg+KB/AgBQcIp0gL+Sv7+/GjRooEOHDjk+F3/lkfSUlBTHUfnQ0FBlZWUpNTX1qmOuxtfXV2XKlHG6AACA/NE/AQAoOJYK8JmZmdq/f7/CwsJUtWpVhYaGKi4uznF7VlaW4uPj1bx5c0lS48aN5e3t7TQmKSlJe/fudYwBAAAAAMAKivRZ6EeNGqUuXbqocuXKSklJ0auvvqr09HRFR0fLZrMpJiZGEydOVI0aNVSjRg1NnDhRpUqVUu/evSVJdrtd/fr108iRI1WuXDkFBgZq1KhRjrfkAwAAAABgFUU6wJ84cUKPPfaY/vzzT5UvX17NmjXT5s2bFRERIUkaM2aMzp8/r4EDByo1NVVNmzbV6tWrFRAQ4Kgxbdo0lShRQj179tT58+fVpk0bzZs3T15eXoW1WQAAAAAA3DCbMcYU9iSsID09XXa7XWlpaWr1yjK36+1480n3JwUAQBFH/wQAwHMs9Rl4AAAAAACKKwI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACShT2BPC3xqPnu11jx5tPFnhNAAAAAEDh4Ag8AAAAAAAWwBF43BCO6gMAAABA4SDAAzeAf2AAQPHA8z0AoCgqVgH+/fff15tvvqmkpCTVq1dP06dP13333VfY00IBscqLL6vMEwCKKp5HAQDFRbEJ8EuWLFFMTIzef/993XPPPZo1a5Y6deqkH3/8UZUrVy7s6RV7vPjyLB5PACgeeL4HgOKl2AT4qVOnql+/fvrXv/4lSZo+fbpWrVqlmTNnatKkSYU8O6Do40UiALiH51EAgLuKRYDPysrSjh079Oyzzzotb9++vTZt2lRIswJQUIrz1zJaZZ4Aih93n5/yem7iOQ9AcVMsAvyff/6p7OxshYSEOC0PCQlRcnJynvfJzMxUZmam43paWpokKT09XdmZ592eU3p6utP14lyzoOpSs+jXlKT7X/jU7brrX33M6bpVtt8q214Q8yyoutT0XM2AgADZbLYbrnGz+6dknX3eKjULap93d64363dvhf3TSjU9UdfKPQTFk6s99LqYYuDXX381ksymTZuclr/66qumVq1aed5n/PjxRhIXLly4cOFSLC9paWku9Vz6JxcuXLhwKe4XV3vo9bAZY4z+4bKyslSqVCl99tln6tGjh2P5sGHDlJCQoPj4+Fz3ufIIQk5Ojk6dOqVy5crl+9+U9PR0VapUScePH1eZMmU8tg0FUZeaRb9mQdWlZtGvWVB1qVn0axZU3Rut6akj8NfbP12Z4/UozjULqi41+T1Rk989NfNXkEfgi8Vb6H18fNS4cWPFxcU5Bfi4uDh169Ytz/v4+vrK19fXadmtt9563essU6aMR3fugqxLzaJfs6DqUrPo1yyoutQs+jULqm5BzfUSd/unZJ3ttkrNgqpLTX5P1CzadalZ9Gu6olgEeEkaMWKEoqKi1KRJE0VGRmr27Nk6duyYBgwYUNhTAwAAAADgmopNgO/Vq5dOnjypl19+WUlJSapfv75WrlypiIiIwp4aAAAAAADXVGwCvCQNHDhQAwcOLNB1+Pr6avz48bnePlgU61Kz6NcsqLrULPo1C6ouNYt+zYKqW1Bz9SSrbLdVahZUXWrye6Km51hlrtQsOv2zWJzEDgAAAAAAq7ulsCcAAAAAAACujQAPAAAAAIAFEOABAAAAALAAAryHvf/++6patapKliypxo0ba8OGDW7VW79+vbp06aLw8HDZbDYtW7bMrXqTJk3SXXfdpYCAAAUHB6t79+46cOCAWzUlaebMmWrYsKHj+xEjIyP1zTffuF33kkmTJslmsykmJsatOhMmTJDNZnO6hIaGuj2/X3/9VU888YTKlSunUqVK6fbbb9eOHTtcrlelSpVc87TZbBo0aJDLNf/66y+98MILqlq1qvz8/FStWjW9/PLLysnJcbmmJJ05c0YxMTGKiIiQn5+fmjdvrm3btt1QjWv9nRtjNGHCBIWHh8vPz08tW7bUvn373Kr5xRdfqEOHDgoKCpLNZlNCQoJb87x48aLGjh2rBg0ayN/fX+Hh4XryySf122+/uTXPCRMmqHbt2vL391fZsmXVtm1bbdmyxa2al3v66adls9k0ffr0a2z9tev26dMn199ss2bN3J7r/v371bVrV9ntdgUEBKhZs2Y6duyYyzXz2rdsNpvefPNNl2tmZGRo8ODBqlixovz8/FSnTh3NnDnTrW3//fff1adPH4WHh6tUqVLq2LGjDh06lG/N63mOd2V/uhmKev+UCqaHFnT/lDzTQwuqf0r0UHd6aEH0z+upSw/92/X2UKv0z+upSw8t/B5KgPegJUuWKCYmRs8//7x27dql++67T506dbrmjpKfs2fPqlGjRpoxY4ZH5hgfH69BgwZp8+bNiouL019//aX27dvr7NmzbtWtWLGiXn/9dW3fvl3bt29X69at1a1bN4/8QW/btk2zZ89Ww4YN3a4lSfXq1VNSUpLjsmfPHrfqpaam6p577pG3t7e++eYb/fjjj5oyZYpuvfVWl2tu27bNaY5xcXGSpEceecTlmm+88YY++OADzZgxQ/v379fkyZP15ptv6t1333W5piT961//UlxcnBYsWKA9e/aoffv2atu2rX799dfrrnGtv/PJkydr6tSpmjFjhrZt26bQ0FC1a9dOZ86ccbnm2bNndc899+j111/3yDzPnTunnTt36sUXX9TOnTv1xRdf6ODBg+ratavLNSWpZs2amjFjhvbs2aONGzeqSpUqat++vf744w+Xa16ybNkybdmyReHh4fmOu5G6HTt2dPrbXblypVs1f/75Z917772qXbu21q1bp//7v//Tiy++qJIlS7pc8/L5JSUl6aOPPpLNZtPDDz/scs3hw4crNjZWCxcu1P79+zV8+HANGTJEX375pUs1jTHq3r27fvnlF3355ZfatWuXIiIi1LZt23yfr6/nOd6V/amgWaF/SgXTQwuyf0qe7aGe7p8SPdTdHloQ/fN66tJDb6yHWqV/Xk9demgR6KEGHnP33XebAQMGOC2rXbu2efbZZz1SX5JZunSpR2pdkpKSYiSZ+Ph4j9Y1xpiyZcua//7v/3arxpkzZ0yNGjVMXFycadGihRk2bJhb9caPH28aNWrkVo0rjR071tx7770erXmlYcOGmdtuu83k5OS4XOPBBx80ffv2dVr20EMPmSeeeMLlmufOnTNeXl7m66+/dlreqFEj8/zzz7tU88q/85ycHBMaGmpef/11x7ILFy4Yu91uPvjgA5dqXi4xMdFIMrt27XJrnnnZunWrkWSOHj3qsZppaWlGklmzZo1bNU+cOGEqVKhg9u7dayIiIsy0adOuq15+daOjo023bt1uqM61avbq1cutv9HreUy7detmWrdu7VbNevXqmZdfftlp2Z133mleeOEFl2oeOHDASDJ79+51LPvrr79MYGCg+fDDD697rlc+x3tifyoIVuyfxhRcD/VE/zTGsz20IPqnMfRQT/bQguifedW9HD30xnuoVfrn1epeiR5683soR+A9JCsrSzt27FD79u2dlrdv316bNm0qpFldW1pamiQpMDDQYzWzs7O1ePFinT17VpGRkW7VGjRokB588EG1bdvWQ7OTDh06pPDwcFWtWlWPPvqofvnlF7fqLV++XE2aNNEjjzyi4OBg3XHHHfrwww89NNu//7YWLlyovn37ymazuVzn3nvv1bfffquDBw9Kkv7v//5PGzdu1AMPPOByzb/++kvZ2dm5/pvr5+enjRs3ulz3comJiUpOTnbat3x9fdWiRYsivW9Jf+9fNpvNrSNJl8vKytLs2bNlt9vVqFEjl+vk5OQoKipKo0ePVr169Twyt0vWrVun4OBg1axZU/3791dKSorLtXJycrRixQrVrFlTHTp0UHBwsJo2beqRt0Jf8vvvv2vFihXq16+fW3XuvfdeLV++XL/++quMMfruu+908OBBdejQwaV6mZmZkuS0b3l5ecnHx+eG9q0rn+OL4v5k1f4peb6HerJ/Sp7voZ7unxI9tCB7aFHc329EceuhVuufEj1UKpx9igDvIX/++aeys7MVEhLitDwkJETJycmFNKv8GWM0YsQI3Xvvvapfv77b9fbs2aPSpUvL19dXAwYM0NKlS1W3bl2X6y1evFg7d+7UpEmT3J7bJU2bNtX8+fO1atUqffjhh0pOTlbz5s118uRJl2v+8ssvmjlzpmrUqKFVq1ZpwIABGjp0qObPn++ROS9btkynT59Wnz593KozduxYPfbYY6pdu7a8vb11xx13KCYmRo899pjLNQMCAhQZGalXXnlFv/32m7Kzs7Vw4UJt2bJFSUlJbs33kkv7j5X2LUm6cOGCnn32WfXu3VtlypRxq9bXX3+t0qVLq2TJkpo2bZri4uIUFBTkcr033nhDJUqU0NChQ92a15U6deqkRYsWae3atZoyZYq2bdum1q1bOxrpjUpJSVFGRoZef/11dezYUatXr1aPHj300EMPKT4+3iNz/vjjjxUQEKCHHnrIrTrvvPOO6tatq4oVK8rHx0cdO3bU+++/r3vvvdelerVr11ZERITGjRun1NRUZWVl6fXXX1dycvJ171t5PccXxf3Jiv1T8mwP9XT/lDzfQwuif0r00ILsoUVxf79exa2HWrF/SvTQS272PlXipq2pmLjyv7vGGLf+41uQBg8erN27d3vsSGmtWrWUkJCg06dP6/PPP1d0dLTi4+NdehFy/PhxDRs2TKtXr77mZ3VuRKdOnRw/N2jQQJGRkbrtttv08ccfa8SIES7VzMnJUZMmTTRx4kRJ0h133KF9+/Zp5syZevLJJ92e85w5c9SpU6fr/pzy1SxZskQLFy7UJ598onr16ikhIUExMTEKDw9XdHS0y3UXLFigvn37qkKFCvLy8tKdd96p3r17a+fOnW7N90pW2rcuXryoRx99VDk5OXr//ffdrteqVSslJCTozz//1IcffqiePXtqy5YtCg4OvuFaO3bs0Ntvv62dO3d6/PHr1auX4+f69eurSZMmioiI0IoVK1xq7pdODtWtWzcNHz5cknT77bdr06ZN+uCDD9SiRQu35/zRRx/p8ccfd/t55p133tHmzZu1fPlyRUREaP369Ro4cKDCwsJcOvrp7e2tzz//XP369VNgYKC8vLzUtm1bp+ewa8nvOb4o7k9FcU758WQP9WT/lAqmhxZE/5TooTejh1pt3yqOPdSK/VOih15ys/cpjsB7SFBQkLy8vHL99yUlJSXXf2mKgiFDhmj58uX67rvvVLFiRY/U9PHxUfXq1dWkSRNNmjRJjRo10ttvv+1SrR07diglJUWNGzdWiRIlVKJECcXHx+udd95RiRIllJ2d7ZE5+/v7q0GDBtc8K2V+wsLCcr3IqlOnjlsnX7rk6NGjWrNmjf71r3+5XWv06NF69tln9eijj6pBgwaKiorS8OHD3T46c9tttyk+Pl4ZGRk6fvy4tm7dqosXL6pq1apuz1mS4yzHVtm3Ll68qJ49eyoxMVFxcXFuHzmQ/v47rV69upo1a6Y5c+aoRIkSmjNnjku1NmzYoJSUFFWuXNmxbx09elQjR45UlSpV3J7r5cLCwhQREeHy/hUUFKQSJUoU2P61YcMGHThwwO396/z583ruuec0depUdenSRQ0bNtTgwYPVq1cvvfXWWy7Xbdy4sSPUJSUlKTY2VidPnryufetqz/FFcX+yWv+UPN9DPdk/pZvTQz3RPyV6aEH20KK4v18LPfRvRb1/SvTQy93sfYoA7yE+Pj5q3Lix40ynl8TFxal58+aFNKvcjDEaPHiwvvjiC61du9ZjIetq63L1rT9t2rTRnj17lJCQ4Lg0adJEjz/+uBISEuTl5eWROWZmZmr//v0KCwtzucY999yT6ysmDh48qIiICHenp7lz5yo4OFgPPvig27XOnTunW25x3uW9vLzc/gqcS/z9/RUWFqbU1FStWrVK3bp180jdqlWrKjQ01GnfysrKUnx8fJHat6T/vPA4dOiQ1qxZo3LlyhXIetzZt6KiorR7926nfSs8PFyjR4/WqlWrPDrPkydP6vjx4y7vXz4+PrrrrrsKbP+aM2eOGjdu7NZnIaW/f+8XL14ssP3LbrerfPnyOnTokLZv357vvnWt5/iiuD9ZpX9KN6+HurOPSzenh3qif0r00EsKoocWxf09P/TQ/yjq/VOih15SGPsUb6H3oBEjRigqKkpNmjRRZGSkZs+erWPHjmnAgAEu18zIyNDhw4cd1xMTE5WQkKDAwEBVrlz5husNGjRIn3zyib788ksFBAQ4/oNkt9vl5+fn8jyfe+45derUSZUqVdKZM2e0ePFirVu3TrGxsS7VCwgIyPWZQn9/f5UrV86tzxqOGjVKXbp0UeXKlZWSkqJXX31V6enpbr39bfjw4WrevLkmTpyonj17auvWrZo9e7Zmz57tck3p77c/zZ07V9HR0SpRwv1dtUuXLnrttddUuXJl1atXT7t27dLUqVPVt29ft+quWrVKxhjVqlVLhw8f1ujRo1WrVi3913/913XXuNbfeUxMjCZOnKgaNWqoRo0amjhxokqVKqXevXu7XPPUqVM6duyY4ztmLzW50NDQq363cX41w8PD9f/9f/+fdu7cqa+//lrZ2dmO/SswMFA+Pj43XLNcuXJ67bXX1LVrV4WFhenkyZN6//33deLEiXy/Dula237liyJvb2+FhoaqVq1aV615rbqBgYGaMGGCHn74YYWFhenIkSN67rnnFBQUpB49erg819GjR6tXr166//771apVK8XGxuqrr77SunXrXK4pSenp6frss880ZcqUfLf5emu2aNFCo0ePlp+fnyIiIhQfH6/58+dr6tSpLtf87LPPVL58eVWuXFl79uzRsGHD1L1791wne7vctZ7jL30X+I3uTwXNCv1TKpge6un+KRVMDy2I/inRQ93toQXRP6+nLj30P66nh1qlf15PXYkeWug99Kad776YeO+990xERITx8fExd955p9tfLfPdd98ZSbku0dHRLtXLq5YkM3fuXLfm2bdvX8d2ly9f3rRp08asXr3arZpX8sTXyPXq1cuEhYUZb29vEx4ebh566CGzb98+t+f21Vdfmfr16xtfX19Tu3ZtM3v2bLdrrlq1ykgyBw4ccLuWMcakp6ebYcOGmcqVK5uSJUuaatWqmeeff95kZma6VXfJkiWmWrVqxsfHx4SGhppBgwaZ06dP31CNa/2d5+TkmPHjx5vQ0FDj6+tr7r//frNnzx63as6dOzfP28ePH+9SzUtfpZPX5bvvvnOp5vnz502PHj1MeHi48fHxMWFhYaZr165m69atbm37la73K3Dyq3vu3DnTvn17U758eePt7W0qV65soqOjzbFjx9ye65w5c0z16tVNyZIlTaNGjcyyZcvcrjlr1izj5+d33X+r16qZlJRk+vTpY8LDw03JkiVNrVq1zJQpU/L92qpr1Xz77bdNxYoVHY/nCy+8cM399Xqe413Zn26Got4/jSmYHnoz+qcx7vfQguqfxtBD3emhBdE/r6cuPfQ/rqeHWqV/Xm9demjh9lDb/5ssAAAAAAAowvgMPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCAB9xks9m0bNkyj9Vr2bKlYmJiPFbP0/r06aPu3bsX9jQsLyoqShMnTrzp6123bp1sNptOnz4tSZo3b55uvfXWAlnXhAkTdPvttzuujxo1SkOHDi2QdQGwHvonXEH/RHFHgMc/SnJysoYMGaJq1arJ19dXlSpVUpcuXfTtt98W2DqTkpLUqVOnAqtf1Lz99tuaN2+e47onXjCtX79eXbp0UXh4uEsv6Ir6i7Yr7d69WytWrNCQIUMKeyrq1auXDh486Lh+5YsGTxozZozmzp2rxMTEAqkPwHX0z4JH/3Qf/ZP+CQI8/kGOHDmixo0ba+3atZo8ebL27Nmj2NhYtWrVSoMGDXK5rjFGf/31V67lWVlZkqTQ0FD5+vq6XN/TrjZfT7Hb7R7/j/PZs2fVqFEjzZgxw6N1i6oZM2bokUceUUBAgEv39+Tv2M/PT8HBwR6pdS3BwcFq3769Pvjgg5uyPgDXh/75N/pn0Uf/pH9CkgH+ITp16mQqVKhgMjIyct2WmppqjDEmMTHRSDK7du1yuk2S+e6774wxxnz33XdGkomNjTWNGzc23t7eZu3ataZFixZm0KBBZvjw4aZcuXLm/vvvN8YYI8ksXbrUUe/48eOmV69epmzZsqZUqVKmcePGZvPmzcYYY6Kjo023bt2c5jZs2DDTokULx/UWLVqYYcOGOa4vWLDANG7c2JQuXdqEhISYxx57zPz++++O26823ytdGnfpsTDGmF27dhlJJjEx0RhjzNy5c43dbjexsbGmdu3axt/f33To0MH89ttvjvtcvg3R0dFGktMlMTHRnDp1yvTu3dsEBQWZkiVLmurVq5uPPvoo15zycuXjecl7771nqlevbnx9fU1wcLB5+OGH851DXq5WwxhjcnJyzBtvvGGqVq1qSpYsaRo2bGg+++yzPB/n22+/3ZQsWdK0atXK/P7772blypWmdu3aJiAgwDz66KPm7NmzV92+7Oxsc+utt5qvv/7aafmFCxfM6NGjTcWKFY2Pj4+pXr26+e///u9c6778d3ytORtjzIoVK0yNGjVMyZIlTcuWLc3cuXOd/g4u/c4v/XzlYzl37lxjjDGnT582/fv3N+XLlzcBAQGmVatWJiEhwWldkyZNMsHBwaZ06dKmb9++ZuzYsaZRo0ZOY+bNm2cqVap01ccHwM1H/6R/0j/pn7COEgXxTwHgZjt16pRiY2P12muvyd/fP9ftrvzHe8yYMXrrrbdUrVo1x/0//vhjPfPMM/r+++9ljMl1n4yMDLVo0UIVKlTQ8uXLFRoaqp07dyonJ+eG139JVlaWXnnlFdWqVUspKSkaPny4+vTpo5UrV15zvq44d+6c3nrrLS1YsEC33HKLnnjiCY0aNUqLFi3KNfbtt9/WwYMHVb9+fb388suSpPLly2vYsGH68ccf9c033ygoKEiHDx/W+fPnXZ7T9u3bNXToUC1YsEDNmzfXqVOntGHDhnzncCM1JOmFF17QF198oZkzZ6pGjRpav369nnjiCZUvX14tWrRwjJswYYJmzJihUqVKqWfPnurZs6d8fX31ySefKCMjQz169NC7776rsWPH5rktu3fv1unTp9WkSROn5U8++aR++OEHvfPOO2rUqJESExP1559/Oo258nd8rTkfP35cDz30kAYMGKBnnnlG27dv18iRI6/6OPfq1Ut79+5VbGys1qxZI+nvI0bGGD344IMKDAzUypUrZbfbNWvWLLVp00YHDx5UYGCg/ud//kfjx4/Xe++9p/vuu08LFizQO++8o2rVqjmt4+6779bx48d19OhRRUREXHUuAG4O+if9k/5J/4TFFO7/DwDP2LJli5Fkvvjii3zH3cgRhGXLljndt0WLFub222/PVVOX/cd71qxZJiAgwJw8eTLP9btyBOFKW7duNZLMmTNn8p3vla73CIIkc/jwYceY9957z4SEhFx1G/Kab5cuXcx//dd/5Tufq1EeRxA+//xzU6ZMGZOenp7nfa71mF2rRkZGhilZsqTZtGmT0/J+/fqZxx57zBjzn8dvzZo1jtsnTZpkJJmff/7Zsezpp582HTp0uOo8li5dary8vExOTo5j2YEDB4wkExcXl+d98vodX8+cx40bZ+rUqeO0rrFjx171CIIxxowfPz7Xf/2//fZbU6ZMGXPhwgWn5bfddpuZNWuWMcaYyMhIM2DAAKfbmzZtmqtWWlqakWTWrVuX57YCuLnon/RP+mfuOdM/UZTxGXj8I5j/9998m83msZpX/of3assul5CQoDvuuEOBgYEem8euXbvUrVs3RUREKCAgQC1btpQkHTt27Ibmdr1KlSql2267zXE9LCxMKSkpN1TjmWee0eLFi3X77bdrzJgx2rRpk1tzateunSIiIlStWjVFRUVp0aJFOnfunMdq/Pjjj7pw4YLatWun0qVLOy7z58/Xzz//7FSnYcOGjp9DQkJUqlQpp/+Sh4SE5Pt4nT9/Xr6+vk5/qwkJCfLy8nI6UpGXy3/H1zPn/fv3q1mzZk7rioyMzHcdedmxY4cyMjJUrlw5p3UlJiY6revK2nmty8/PT5Ju+PcHoGDQP+mf7tSgf+aP/omCwFvo8Y9Qo0YN2Ww27d+/P9+vaLnllr//Z2Uue/vexYsX8xyb11sJ81p2uUtPrvmt31zx1sGrrV/6++Q07du3V/v27bVw4UKVL19ex44dU4cOHRwnAbreuV3vtnt7eztdt9lseb7dMT+dOnXS0aNHtWLFCq1Zs0Zt2rTRoEGD9NZbb91QnUsCAgK0c+dOrVu3TqtXr9ZLL72kCRMmaNu2bdf9dsf8alx6i+aKFStUoUIFp/tdeYKlyx8fm82W5+OV31s+g4KCdO7cOWVlZcnHx0fStf9uLrn8d3w9c77R39vV5OTkKCwsTOvWrct1242+3fTUqVOS8n6bJoCbj/5J/3SnBv0zf/RPFASOwOMfITAwUB06dNB7772ns2fP5rr90nd2XnrSS0pKctyWkJDgsXk0bNhQCQkJjifZK5UvX95p3dda/08//aQ///xTr7/+uu677z7Vrl37hv+bf/m6Jc9vu4+Pj7Kzs/NcX58+fbRw4UJNnz5ds2fPdms9JUqUUNu2bTV58mTt3r1bR44c0dq1a/Odw/XWqFu3rnx9fXXs2DFVr17d6VKpUiW35n2lS18x8+OPPzqWNWjQQDk5OYqPj7/uOtcz57p162rz5s1O97vy+pXyeizvvPNOJScnq0SJErnWFRQUJEmqU6fOda1r79698vb2Vr169a57WwEUHPrntdE/6Z8S/RNFBwEe/xjvv/++srOzdffdd+vzzz/XoUOHtH//fr3zzjuOtyL5+fmpWbNmev311/Xjjz9q/fr1euGFFzw2h8cee0yhoaHq3r27vv/+e/3yyy/6/PPP9cMPP0iSWrdure3bt2v+/Pk6dOiQxo8fr7179161XuXKleXj46N3331Xv/zyi5YvX65XXnnFpbldakwTJkzQwYMHtWLFCk2ZMsWlWperUqWKtmzZoiNHjujPP/9UTk6OXnrpJX355Zc6fPiw9u3bp6+//lp16tS5ao2MjAwlJCQ4XhAlJiYqISHB8TbHr7/+Wu+8844SEhJ09OhRzZ8/Xzk5OapVq9ZV53Cl/GoEBARo1KhRGj58uD7++GP9/PPP2rVrl9577z19/PHHbj9GlytfvrzuvPNObdy40bGsSpUqio6OVt++fbVs2TIlJiZq3bp1+p//+Z+r1rmeOQ8YMEA///yzRowYoQMHDuiTTz5x+g7ivFSpUsXx+P/555/KzMxU27ZtFRkZqe7du2vVqlU6cuSINm3apBdeeEHbt2+XJA0bNkwfffSRPvroIx08eFDjx4/Xvn37ctXfsGGD7rvvvus+agKg4NE/80f/pH/SP1GkFM5H74GC8dtvv5lBgwaZiIgI4+PjYypUqGC6du3qOMGOMcb8+OOPplmzZsbPz8/cfvvtZvXq1XmehOfyk9UYc/UTveiKk8YcOXLEPPzww6ZMmTKmVKlSpkmTJmbLli2O21966SUTEhJi7Ha7GT58uBk8eHC+J+H55JNPTJUqVYyvr6+JjIw0y5cvdzqR0NXmm5eNGzeaBg0amJIlS5r77rvPfPbZZ3l+Dc7lli5dai5/qrjyJDwHDhxwPJ6Xar3yyiumTp06xs/PzwQGBppu3bqZX3755arzurQNV16io6ONMcZs2LDBtGjRwpQtW9b4+fmZhg0bmiVLluQ7hytdq0ZOTo55++23Ta1atYy3t7cpX7686dChg4mPj7/q45zX45XXSWyu9MEHH5hmzZo5LTt//rwZPny4CQsLc3wNzqWvDrra7/haczbGmK+++srx1T/33Xef+eijj/I9Cc+FCxfMww8/bG699Vanr8FJT083Q4YMMeHh4cbb29tUqlTJPP744+bYsWOO+7722msmKCjIlC5d2kRHR5sxY8bkeixq1qxpPv3003wfHwA3H/0zf/RP+if9E0WFzRgPfcgDAHBdLly4oFq1amnx4sUunRTHqlasWKHRo0dr9+7dKlGCU7AAAG4M/ZP+Cd5CDwA3XcmSJTV//vxc31P7T3f27FnNnTuXFx8AAJfQP+mfkDgCDwAAAACABXAEHgAAAAAACyDAAwAAAABgAQR4AAAAAAAsgAAPAAAAAIAFEOABAAAAALAAAjwAAAAAABZAgAcAAAAAwAII8AAAAAAAWAABHgAAAAAAC/j/ASU7y/DgiYAqAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", 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", 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", 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AAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAJcG+CtXrui1115T2bJl5e3trXLlyumNN95QZmamfR1jjMaMGaPQ0FB5e3urYcOG2rdvn0OdtLQ0DRgwQCVKlJCPj49atWqlX3/9Nb+fDgAAAAAAecalAf6dd97RBx98oBkzZujAgQOaMGGCJk6cqOnTp9vXmTBhgqZMmaIZM2Zo+/btCg4OVtOmTXXu3Dn7OlFRUVqxYoUWL16sjRs36vz583ruueeUkZHhiqcFAAAAAIDTubvywX/44Qe1bt1azz77rCSpTJky+uyzz7Rjxw5JV/e+T5s2TSNHjlTbtm0lSfPnz1dQUJAWLVqkPn36KDU1VXPmzNEnn3yiJk2aSJIWLlyosLAwrV27Vs2bN3fNkwMAAAAAwIlcuge+fv36+vbbb3Xw4EFJ0o8//qiNGzfqmWeekSQdOXJESUlJatasmf0+Xl5eioyM1ObNmyVJcXFxunz5ssM6oaGhqlq1qn2dG6Wlpens2bMOFwAAcPeYqQAA5B+XBvjhw4erU6dOqlSpkjw8PFSrVi1FRUWpU6dOkqSkpCRJUlBQkMP9goKC7LclJSXJ09NTxYsXz3adG40bN05+fn72S1hYmLOfGgAA9wVmKgAA+celAX7JkiVauHChFi1apJ07d2r+/PmaNGmS5s+f77CezWZzuG6MybLsRrdaZ8SIEUpNTbVfTpw4kbsnAgDAfYqZCgBA/nHpZ+Cjo6P16quvqmPHjpKkatWq6dixYxo3bpx69Oih4OBgSVf3soeEhNjvl5ycbN8rHxwcrPT0dKWkpDjshU9OTla9evVu+rheXl7y8vLKq6cFAMB9g5kKAED+ceke+IsXL6pQIccW3Nzc7F8jV7ZsWQUHBysmJsZ+e3p6umJjY+3hPCIiQh4eHg7rJCYmau/evdkGeAAAAAAArMale+Bbtmypt99+W6VLl1aVKlW0a9cuTZkyRb169ZJ09dD5qKgojR07VhUqVFCFChU0duxYFSlSRJ07d5Yk+fn5qXfv3hoyZIgCAgLk7++voUOHqlq1avaz0gMAAAAAYHUuDfDTp0/XqFGj1LdvXyUnJys0NFR9+vTR66+/bl9n2LBhunTpkvr27auUlBQ99thjWrNmjXx9fe3rTJ06Ve7u7mrfvr0uXbqkxo0ba968eXJzc3PF0wIAAAAAwOlsxhjj6iZc7ezZs/Lz81NqaqqKFSvm6nZQQEVEL3BKnbiJ3Z1SBwAKImYqAAB5x6WfgQcAAAAAAHeGAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACzA3dUNAACAe1NE9AKn1Imb2N0pdQAAsDr2wAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEuD/AnT55U165dFRAQoCJFiqhmzZqKi4uz326M0ZgxYxQaGipvb281bNhQ+/btc6iRlpamAQMGqESJEvLx8VGrVq3066+/5vdTAQAAAAAgz7i78sFTUlL0xBNPqFGjRvr3v/+twMBAHT58WA888IB9nQkTJmjKlCmaN2+eHnroIb311ltq2rSpEhIS5OvrK0mKiorSl19+qcWLFysgIEBDhgzRc889p7i4OLm5ubno2cEVIqIXOKVO3MTuTqkDAAAAAM7i0gD/zjvvKCwsTHPnzrUvK1OmjP3fxhhNmzZNI0eOVNu2bSVJ8+fPV1BQkBYtWqQ+ffooNTVVc+bM0SeffKImTZpIkhYuXKiwsDCtXbtWzZs3z9fnBAAAAABAXnDpIfQrV65U7dq11a5dOwUGBqpWrVr66KOP7LcfOXJESUlJatasmX2Zl5eXIiMjtXnzZklSXFycLl++7LBOaGioqlatal/nRmlpaTp79qzDBQAA3D1mKgAA+celAf6XX37RrFmzVKFCBa1evVovv/yyXnnlFS1YcPUw6KSkJElSUFCQw/2CgoLstyUlJcnT01PFixfPdp0bjRs3Tn5+fvZLWFiYs58aAAD3BWYqAAD5x6UBPjMzU4888ojGjh2rWrVqqU+fPnrppZc0a9Ysh/VsNpvDdWNMlmU3utU6I0aMUGpqqv1y4sSJ3D0RAADuU8xUAADyj0sDfEhIiCpXruyw7OGHH9bx48clScHBwZKUZU96cnKyfa98cHCw0tPTlZKSku06N/Ly8lKxYsUcLgAA4O4xUwEAyD8uDfBPPPGEEhISHJYdPHhQ4eHhkqSyZcsqODhYMTEx9tvT09MVGxurevXqSZIiIiLk4eHhsE5iYqL27t1rXwcAAAAAAKtz6VnoBw0apHr16mns2LFq3769tm3bptmzZ2v27NmSrh46HxUVpbFjx6pChQqqUKGCxo4dqyJFiqhz586SJD8/P/Xu3VtDhgxRQECA/P39NXToUFWrVs1+VnoAAAAAAKzOpQG+Tp06WrFihUaMGKE33nhDZcuW1bRp09SlSxf7OsOGDdOlS5fUt29fpaSk6LHHHtOaNWvs3wEvSVOnTpW7u7vat2+vS5cuqXHjxpo3bx7fAQ8AAAAAuGfYjDHG1U242tmzZ+Xn56fU1FQ+u2dxEdELnFInbmL3fK0NAPeK62dqozc/d0pNfm8CAHCVSz8DDwAAAAAA7gwBHgAAAAAACyDAAwAAAABgAQR4AAAAAAAsgAAPAAAAAIAFEOABAAAAALAAAjwAAAAAABZAgAcAAAAAwAII8AAAAAAAWAABHgAAAAAACyDAAwAAAABgAQR4AAAAAAAsgAAPAAAAAIAFEOABAAAAALAAAjwAAAAAABZAgAcAAAAAwAII8AAAAAAAWAABHgAAAAAACyDAAwAAAABgAQR4AAAAAAAsgAAPAAAAAIAFEOABAAAAALAAAjwAAAAAABZAgAcAAAAAwAII8AAAAAAAWAABHgAAAAAACyDAAwAAAABgAQR4AAAAAAAsgAAPAAAAAIAFEOABAAAAALAAAjwAAAAAABaQowD/1FNP6cyZM1mWnz17Vk899VRuewIAAAAAADfIUYBfv3690tPTsyz/888/tWHDhlw3BQAAAAAAHLnfzcq7d++2/3v//v1KSkqyX8/IyNCqVav04IMPOq87AAAAAAAg6S4DfM2aNWWz2WSz2W56qLy3t7emT5/utOYAAAAAAMBVdxXgjxw5ImOMypUrp23btqlkyZL22zw9PRUYGCg3NzenNwkAAAAAwP3urgJ8eHi4JCkzMzNPmgEAAAAAADd3VwH+egcPHtT69euVnJycJdC//vrruW4MAAAAAAD8JUcB/qOPPtI//vEPlShRQsHBwbLZbPbbbDYbAR4AAAAAACfLUYB/66239Pbbb2v48OHO7gcAAAAAANxEjr4HPiUlRe3atXN2LwAAAAAAIBs5CvDt2rXTmjVrnN0LAAAAAADIRo4OoS9fvrxGjRqlLVu2qFq1avLw8HC4/ZVXXnFKcwAAAAAA4KocBfjZs2eraNGiio2NVWxsrMNtNpuNAA8AAAAAgJPlKMAfOXLE2X0AAAAAAIBbyNFn4AEAAAAAQP7K0R74Xr163fL2jz/+OEfNAAAAAACAm8tRgE9JSXG4fvnyZe3du1dnzpzRU0895ZTGAAAAAADAX3IU4FesWJFlWWZmpvr27aty5crluikAAAAAAODIaZ+BL1SokAYNGqSpU6c6qyQAAAAAAPg/Tj2J3eHDh3XlyhVnlgQAAAAAAMrhIfSDBw92uG6MUWJior7++mv16NHDKY0BAAAAAIC/5CjA79q1y+F6oUKFVLJkSU2ePPm2Z6gHAAAAAAB3L0cBft26dc7uAwAAAAAA3EKOAvw1//nPf5SQkCCbzaaHHnpIJUuWdFZfAAAAAADgOjk6id2FCxfUq1cvhYSEqEGDBnryyScVGhqq3r176+LFi87uEQAAAACA+16OAvzgwYMVGxurL7/8UmfOnNGZM2f0xRdfKDY2VkOGDHF2jwAAAAAA3PdydAj9smXL9K9//UsNGza0L3vmmWfk7e2t9u3ba9asWc7qDwAAAAAAKId74C9evKigoKAsywMDAzmEHgAAAACAPJCjAF+3bl2NHj1af/75p33ZpUuX9D//8z+qW7eu05oDAAAAAABX5egQ+mnTpqlFixYqVaqUatSoIZvNpvj4eHl5eWnNmjXO7hEAAAAAgPtejgJ8tWrVdOjQIS1cuFA//fSTjDHq2LGjunTpIm9vb2f3CAAAAADAfS9HAX7cuHEKCgrSSy+95LD8448/1n/+8x8NHz7cKc0BAAAAAICrcvQZ+A8//FCVKlXKsrxKlSr64IMPct0UAAAAAABwlKMAn5SUpJCQkCzLS5YsqcTExFw3BQAAAAAAHOUowIeFhWnTpk1Zlm/atEmhoaG5bgoAAAAAADjK0WfgX3zxRUVFReny5ct66qmnJEnffvuthg0bpiFDhji1QQAAAAAAkMMAP2zYMJ0+fVp9+/ZVenq6JKlw4cIaPny4RowY4dQGAQAAAABADgO8zWbTO++8o1GjRunAgQPy9vZWhQoV5OXl5ez+AAAAAACAchjgrylatKjq1KnjrF4AAAAAAEA2cnQSOwAAAAAAkL8I8AAAAAAAWAABHgAAAAAACyDAAwAAAABgAQR4AAAAAAAsoMAE+HHjxslmsykqKsq+zBijMWPGKDQ0VN7e3mrYsKH27dvncL+0tDQNGDBAJUqUkI+Pj1q1aqVff/01n7sHAAAAACBvFYgAv337ds2ePVvVq1d3WD5hwgRNmTJFM2bM0Pbt2xUcHKymTZvq3Llz9nWioqK0YsUKLV68WBs3btT58+f13HPPKSMjI7+fBgAAAAAAecblAf78+fPq0qWLPvroIxUvXty+3BijadOmaeTIkWrbtq2qVq2q+fPn6+LFi1q0aJEkKTU1VXPmzNHkyZPVpEkT1apVSwsXLtSePXu0du1aVz0lAAAAAACczuUBvl+/fnr22WfVpEkTh+VHjhxRUlKSmjVrZl/m5eWlyMhIbd68WZIUFxeny5cvO6wTGhqqqlWr2te5mbS0NJ09e9bhAgAA7h4zFQCA/OPSAL948WLt3LlT48aNy3JbUlKSJCkoKMhheVBQkP22pKQkeXp6Ouy5v3Gdmxk3bpz8/Pzsl7CwsNw+FQAA7kvMVAAA8o/LAvyJEyc0cOBALVy4UIULF852PZvN5nDdGJNl2Y1ut86IESOUmppqv5w4ceLumgcAAJKYqQAA5Cd3Vz1wXFyckpOTFRERYV+WkZGh77//XjNmzFBCQoKkq3vZQ0JC7OskJyfb98oHBwcrPT1dKSkpDnvhk5OTVa9evWwf28vLS15eXs5+SgAA3HeYqQAA5B+X7YFv3Lix9uzZo/j4ePuldu3a6tKli+Lj41WuXDkFBwcrJibGfp/09HTFxsbaw3lERIQ8PDwc1klMTNTevXtvGeABAAAAALAal+2B9/X1VdWqVR2W+fj4KCAgwL48KipKY8eOVYUKFVShQgWNHTtWRYoUUefOnSVJfn5+6t27t4YMGaKAgAD5+/tr6NChqlatWpaT4gEAAAAAYGUuC/B3YtiwYbp06ZL69u2rlJQUPfbYY1qzZo18fX3t60ydOlXu7u5q3769Ll26pMaNG2vevHlyc3NzYecAAAAAADhXgQrw69evd7hus9k0ZswYjRkzJtv7FC5cWNOnT9f06dPztjkAAAAAAFzI5d8DDwAAAAAAbo8ADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAtwd3UDwP0uInqBU+rETezulDoAAAAACib2wAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAE5iB5fgxG35g9cZAAAAuHewBx4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAEEeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAW4NIAP27cONWpU0e+vr4KDAxUmzZtlJCQ4LCOMUZjxoxRaGiovL291bBhQ+3bt89hnbS0NA0YMEAlSpSQj4+PWrVqpV9//TU/nwoAAAAAAHnKpQE+NjZW/fr105YtWxQTE6MrV66oWbNmunDhgn2dCRMmaMqUKZoxY4a2b9+u4OBgNW3aVOfOnbOvExUVpRUrVmjx4sXauHGjzp8/r+eee04ZGRmueFoAAAAAADiduysffNWqVQ7X586dq8DAQMXFxalBgwYyxmjatGkaOXKk2rZtK0maP3++goKCtGjRIvXp00epqamaM2eOPvnkEzVp0kSStHDhQoWFhWnt2rVq3rx5vj8vAAAAAACcrUB9Bj41NVWS5O/vL0k6cuSIkpKS1KxZM/s6Xl5eioyM1ObNmyVJcXFxunz5ssM6oaGhqlq1qn0dAAAAAACszqV74K9njNHgwYNVv359Va1aVZKUlJQkSQoKCnJYNygoSMeOHbOv4+npqeLFi2dZ59r9b5SWlqa0tDT79bNnzzrteQAAcD9hpgIAkH8KzB74/v37a/fu3frss8+y3Gaz2RyuG2OyLLvRrdYZN26c/Pz87JewsLCcNw4AwH2MmQoAQP4pEAF+wIABWrlypdatW6dSpUrZlwcHB0tSlj3pycnJ9r3ywcHBSk9PV0pKSrbr3GjEiBFKTU21X06cOOHMpwMAwH2DmQoAQP5xaYA3xqh///5avny5vvvuO5UtW9bh9rJlyyo4OFgxMTH2Zenp6YqNjVW9evUkSREREfLw8HBYJzExUXv37rWvcyMvLy8VK1bM4QIAAO4eMxUAgPzj0s/A9+vXT4sWLdIXX3whX19f+552Pz8/eXt7y2azKSoqSmPHjlWFChVUoUIFjR07VkWKFFHnzp3t6/bu3VtDhgxRQECA/P39NXToUFWrVs1+VnoAAAAAAKzOpQF+1qxZkqSGDRs6LJ87d6569uwpSRo2bJguXbqkvn37KiUlRY899pjWrFkjX19f+/pTp06Vu7u72rdvr0uXLqlx48aaN2+e3Nzc8uupAAAAAACQp1wa4I0xt13HZrNpzJgxGjNmTLbrFC5cWNOnT9f06dOd2B0AAAAAAAVHgTiJHQAAAAAAuDUCPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYgLurG0DBFhG9INc14iZ2d0InAAAAAHB/Yw88AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAAR4AAAAAAAsgwAMAAAAAYAGchf4GzjjrusSZ1wEAAAAAzsUeeAAAAAAALIAADwAAAACABRDgAQAAAACwAAI8AAAAAAAWQIAHAAAAAMACCPAAAAAAAFgAXyMHAACQD/iqWgBAbrEHHgAAAAAACyDAAwAAAABgARxCDwAAgJtyxmH/HPIPAM7DHngAAAAAACyAPfAAAAD/hxPNAQAKMgI8AACAhfGmAwDcPziEHgAAAAAACyDAAwAAAABgAQR4AAAAAAAsgAAPAAAAAIAFEOABAAAAALAAAjwAAAAAABZAgAcAAAAAwAII8AAAAAAAWAABHgAAAAAACyDAAwAAAABgAQR4AAAAAAAswN3VDQAAANytiOgFTqkTN7G7U+oAAJAfCPD3AP6IAQAAAIB7H4fQAwAAAABgAQR4AAAAAAAsgAAPAAAAAIAFEOABAAAAALAAAjwAAAAAABZAgAcAAAAAwAII8AAAAAAAWADfAw8AAIB8FRG9wCl14iZ2d0odALAK9sADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCABwAAAADAAgjwAAAAAABYAAEeAAAAAAALcHd1AwCQHyKiFzilTtzE7k6pAwAAANwt9sADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFsBZ6AHcNc7oDgAAAOQ/Anw+IvQAt2fF7cSKPQMAAMB6OIQeAAAAAAALIMADAAAAAGABHEIPAAUUh+YDQMHB72QABQEBHgAAAPcEQjaAex2H0AMAAAAAYAEEeAAAAAAALOCeOYR+5syZmjhxohITE1WlShVNmzZNTz75pKvbAgAAAFyCjxQA9557IsAvWbJEUVFRmjlzpp544gl9+OGHatGihfbv36/SpUu7uj0AQC7xRygAAMA9EuCnTJmi3r1768UXX5QkTZs2TatXr9asWbM0btw4F3cHAAUPgRgACgZ+Hzvi9QBuzfIBPj09XXFxcXr11Vcdljdr1kybN292UVcAgPsdf4QCuFfx+w1wHcsH+D/++EMZGRkKCgpyWB4UFKSkpKSb3ictLU1paWn266mpqZKks2fPKiPtklP6Onv2bJZleVW7oPfMa3Hr2vTsiJ7zvm5e184Ledlvg9c+c0rt79/q5HDdaj37+vrKZrPddQ1m6p3VzcvaBb1nXotb16ZnR3lVO69+1+clK/aMq3I6U++IsbiTJ08aSWbz5s0Oy9966y1TsWLFm95n9OjRRhIXLly4cOHC5f8uqampOZrDzFQuXLhw4cLF8ZLTmXonbMYYIwtLT09XkSJFtHTpUj3//PP25QMHDlR8fLxiY2Oz3OfGvQWZmZk6ffq0AgICbvtOydmzZxUWFqYTJ06oWLFiznsieVjbanXzsjY9509tes6f2vSc93XzsnZB69lZe+CZqQWvttXq5mVtes6f2vScP7XpOe/r5rR2Xu6Bt/wh9J6enoqIiFBMTIxDgI+JiVHr1q1veh8vLy95eXk5LHvggQfu6nGLFSvm9P8ceV3banXzsjY9509tes6f2vSc93XzsrYVe74eM9X5rNYzr0X+1Kbn/KlNz/lT22p187r23bB8gJekwYMHq1u3bqpdu7bq1q2r2bNn6/jx43r55Zdd3RoAAAAAAE5xTwT4Dh066NSpU3rjjTeUmJioqlWr6ptvvlF4eLirWwMAAAAAwCnuiQAvSX379lXfvn3z/HG8vLw0evToLIcLFuTaVqubl7XpOX9q03P+1KbnvK+bl7Wt2LOzWfE1oOe8r5uXtek5f2rTc/7Upue8r5vXtXPC8iexAwAAAADgflDI1Q0AAAAAAIDbI8ADAAAAAGABBHgAAAAAACyAAH+XZs6cqbJly6pw4cKKiIjQhg0bcl3z+++/V8uWLRUaGiqbzabPP/88941KGjdunOrUqSNfX18FBgaqTZs2SkhIyHXdWbNmqXr16vbvQqxbt67+/e9/O6FjR+PGjZPNZlNUVFSua40ZM0Y2m83hEhwcnPsmJZ08eVJdu3ZVQECAihQpopo1ayouLi7XdcuUKZOlZ5vNpn79+uWq7pUrV/Taa6+pbNmy8vb2Vrly5fTGG28oMzMz1z1L0rlz5xQVFaXw8HB5e3urXr162r59+13VuN02YYzRmDFjFBoaKm9vbzVs2FD79u1zSu3ly5erefPmKlGihGw2m+Lj43Nd9/Llyxo+fLiqVasmHx8fhYaGqnv37vrtt9+c0vOYMWNUqVIl+fj4qHjx4mrSpIm2bt2a67rX69Onj2w2m6ZNm+aUniXpwIEDatWqlfz8/OTr66vHH39cx48fz1Xdm20zNptNEydOzHXP58+fV//+/VWqVCl5e3vr4Ycf1qxZs3Jd9/fff1fPnj0VGhqqIkWK6Omnn9ahQ4duW/dOfsfnZlvJD8xUZurN5MVcZaYyU++0Z2bqX5ipBXOmEuDvwpIlSxQVFaWRI0dq165devLJJ9WiRYvbbhy3c+HCBdWoUUMzZsxwUqdXxcbGql+/ftqyZYtiYmJ05coVNWvWTBcuXMhV3VKlSmn8+PHasWOHduzYoaeeekqtW7d26n/g7du3a/bs2apevbrTalapUkWJiYn2y549e3JdMyUlRU888YQ8PDz073//W/v379fkyZP1wAMP5Lr29u3bHfqNiYmRJLVr1y5Xdd955x198MEHmjFjhg4cOKAJEyZo4sSJmj59eq57lqQXX3xRMTEx+uSTT7Rnzx41a9ZMTZo00cmTJ++4xu22iQkTJmjKlCmaMWOGtm/fruDgYDVt2lTnzp3Lde0LFy7oiSee0Pjx4++439vVvXjxonbu3KlRo0Zp586dWr58uQ4ePKhWrVrlurYkPfTQQ5oxY4b27NmjjRs3qkyZMmrWrJn+85//5KruNZ9//rm2bt2q0NDQO+r3TmofPnxY9evXV6VKlbR+/Xr9+OOPGjVqlAoXLpyrutdvM4mJifr4449ls9n0X//1X7nuedCgQVq1apUWLlyoAwcOaNCgQRowYIC++OKLHNc1xqhNmzb65Zdf9MUXX2jXrl0KDw9XkyZNbvu7+k5+x+dmW8lrzNSrmKmO8mquMlOZqXfaMzP1L8zUAjpTDe7Yo48+al5++WWHZZUqVTKvvvqq0x5DklmxYoXT6l0vOTnZSDKxsbFOr128eHHzv//7v06pde7cOVOhQgUTExNjIiMjzcCBA3Ndc/To0aZGjRq5rnOj4cOHm/r16zu97s0MHDjQ/O1vfzOZmZm5qvPss8+aXr16OSxr27at6dq1a67qGmPMxYsXjZubm/nqq68clteoUcOMHDkyRzVv3CYyMzNNcHCwGT9+vH3Zn3/+afz8/MwHH3yQq9rXO3LkiJFkdu3aleueb2bbtm1Gkjl27JjTa6emphpJZu3atbmu++uvv5oHH3zQ7N2714SHh5upU6feVb/Z1e7QoUOu/8/dyWvRunVr89RTTzmldpUqVcwbb7zhsOyRRx4xr732Wo7rJiQkGElm79699mVXrlwx/v7+5qOPPrqrnm/8He/MbSUvMFOzd7/OVGPyb64yU69ipjJTb1X3RszUgjFT2QN/h9LT0xUXF6dmzZo5LG/WrJk2b97soq7uTmpqqiTJ39/faTUzMjK0ePFiXbhwQXXr1nVKzX79+unZZ59VkyZNnFLvmkOHDik0NFRly5ZVx44d9csvv+S65sqVK1W7dm21a9dOgYGBqlWrlj766CMndOsoPT1dCxcuVK9evWSz2XJVq379+vr222918OBBSdKPP/6ojRs36plnnsl1n1euXFFGRkaWd3y9vb21cePGXNeXpCNHjigpKclhW/Ty8lJkZKRltkXp6vZos9mccrTG9dLT0zV79mz5+fmpRo0auaqVmZmpbt26KTo6WlWqVHFSh1frfv3113rooYfUvHlzBQYG6rHHHnPaoc7X/P777/r666/Vu3dvp9SrX7++Vq5cqZMnT8oYo3Xr1ungwYNq3rx5jmumpaVJksM24+bmJk9Pz7veZm78HV+QtxVm6s3d7zNVyp+5ykz9S0H+PXE3mKnMVOn+makE+Dv0xx9/KCMjQ0FBQQ7Lg4KClJSU5KKu7pwxRoMHD1b9+vVVtWrVXNfbs2ePihYtKi8vL7388stasWKFKleunOu6ixcv1s6dOzVu3Lhc17reY489pgULFmj16tX66KOPlJSUpHr16unUqVO5qvvLL79o1qxZqlChglavXq2XX35Zr7zyihYsWOCkzq/6/PPPdebMGfXs2TPXtYYPH65OnTqpUqVK8vDwUK1atRQVFaVOnTrluravr6/q1q2rN998U7/99psyMjK0cOFCbd26VYmJibmuL8m+vVl1W5SkP//8U6+++qo6d+6sYsWKOaXmV199paJFi6pw4cKaOnWqYmJiVKJEiVzVfOedd+Tu7q5XXnnFKT1ek5ycrPPnz2v8+PF6+umntWbNGj3//PNq27atYmNjnfY48+fPl6+vr9q2beuUeu+9954qV66sUqVKydPTU08//bRmzpyp+vXr57hmpUqVFB4erhEjRiglJUXp6ekaP368kpKS7mqbudnv+IK8rTBTHTFT/5Ifc5WZ+peC/HviTjFTmanX3C8z1T1fH+0ecOM7tcaYXL97mx/69++v3bt3O+0d24oVKyo+Pl5nzpzRsmXL1KNHD8XGxubqD44TJ05o4MCBWrNmzW0/s3O3WrRoYf93tWrVVLduXf3tb3/T/PnzNXjw4BzXzczMVO3atTV27FhJUq1atbRv3z7NmjVL3bt3z3Xf18yZM0ctWrS4q89LZWfJkiVauHChFi1apCpVqig+Pl5RUVEKDQ1Vjx49cl3/k08+Ua9evfTggw/Kzc1NjzzyiDp37qydO3fmuvb1rLotXr58WR07dlRmZqZmzpzptLqNGjVSfHy8/vjjD3300Udq3769tm7dqsDAwBzVi4uL07vvvqudO3c6/XW9dnKn1q1ba9CgQZKkmjVravPmzfrggw8UGRnplMf5+OOP1aVLF6f9Pnnvvfe0ZcsWrVy5UuHh4fr+++/Vt29fhYSE5HjvpoeHh5YtW6bevXvL399fbm5uatKkicPvrDtxq9/xBXlbKci93QozNW9mqpQ/c5WZmpVVt0VmKjP1evfLTGUP/B0qUaKE3NzcsrzDkpycnOWdmIJmwIABWrlypdatW6dSpUo5paanp6fKly+v2rVra9y4capRo4befffdXNWMi4tTcnKyIiIi5O7uLnd3d8XGxuq9996Tu7u7MjIynNK7JPn4+KhatWp3dFbKWwkJCcnyB9bDDz+c65MwXe/YsWNau3atXnzxRafUi46O1quvvqqOHTuqWrVq6tatmwYNGuS0PTR/+9vfFBsbq/Pnz+vEiRPatm2bLl++rLJlyzql/rUzHVtxW7x8+bLat2+vI0eOKCYmxml7CqSr/6fLly+vxx9/XHPmzJG7u7vmzJmT43obNmxQcnKySpcubd8ejx07piFDhqhMmTK56rVEiRJyd3fP021nw4YNSkhIcNp2c+nSJf3zn//UlClT1LJlS1WvXl39+/dXhw4dNGnSpFzVjoiIsIe3xMRErVq1SqdOnbrjbSa73/EFeVthpjpipv4lr+cqM9VRQf49cTvM1KuYqY7uh5lKgL9Dnp6eioiIsJ+19JqYmBjVq1fPRV3dmjFG/fv31/Lly/Xdd9857Zd9do917XMnOdW4cWPt2bNH8fHx9kvt2rXVpUsXxcfHy83NzUndXv2MzIEDBxQSEpKrOk888USWr5g4ePCgwsPDc1X3enPnzlVgYKCeffZZp9S7ePGiChVy3PTd3Nyc9pU31/j4+CgkJEQpKSlavXq1Wrdu7ZS6ZcuWVXBwsMO2mJ6ertjY2AK7LUp//aFx6NAhrV27VgEBAXn6eLndJrt166bdu3c7bI+hoaGKjo7W6tWrc9Wbp6en6tSpk6fbzpw5cxQREZHrzyxec/nyZV2+fDlPtx0/Pz+VLFlShw4d0o4dO267zdzud3xB3laYqbd/rPtxpkp5P1eZqY4K8u+JW2Gm/oWZenP38kzlEPq7MHjwYHXr1k21a9dW3bp1NXv2bB0/flwvv/xyruqeP39eP//8s/36kSNHFB8fL39/f5UuXTrHdfv166dFixbpiy++kK+vr/0dIz8/P3l7e+e47j//+U+1aNFCYWFhOnfunBYvXqz169dr1apVOa4pXf2s142fJfTx8VFAQECuP2M4dOhQtWzZUqVLl1ZycrLeeustnT17NteHtw0aNEj16tXT2LFj1b59e23btk2zZ8/W7Nmzc1X3mszMTM2dO1c9evSQu7tzNteWLVvq7bffVunSpVWlShXt2rVLU6ZMUa9evZxSf/Xq1TLGqGLFivr5558VHR2tihUr6oUXXrjjGrfbJqKiojR27FhVqFBBFSpU0NixY1WkSBF17tw517VPnz6t48eP279P9tpADA4OvuX3HN+qbmhoqP77v/9bO3fu1FdffaWMjAz79ujv7y9PT88c9xwQEKC3335brVq1UkhIiE6dOqWZM2fq119/ve3XI93utbjxDyIPDw8FBwerYsWKt6x7J7Wjo6PVoUMHNWjQQI0aNdKqVav05Zdfav369bmqK0lnz57V0qVLNXny5Nv2eTe1IyMjFR0dLW9vb4WHhys2NlYLFizQlClTclV36dKlKlmypEqXLq09e/Zo4MCBatOmTZYTvN3odr/jr33nd063lbzGTL2KmeooL+cqM/UqZioz9U7rSszUAjlT8/OU9/eC999/34SHhxtPT0/zyCOPOOXrY9atW2ckZbn06NEjV3VvVlOSmTt3bq7q9urVy/4alCxZ0jRu3NisWbMmVzWz46yvvOnQoYMJCQkxHh4eJjQ01LRt29bs27cv9w0aY7788ktTtWpV4+XlZSpVqmRmz57tlLrGGLN69WojySQkJDit5tmzZ83AgQNN6dKlTeHChU25cuXMyJEjTVpamlPqL1myxJQrV854enqa4OBg069fP3PmzJm7qnG7bSIzM9OMHj3aBAcHGy8vL9OgQQOzZ88ep9SeO3fuTW8fPXp0jute+/qcm13WrVuXq54vXbpknn/+eRMaGmo8PT1NSEiIadWqldm2bVuuX4sb3c1X3txJ7Tlz5pjy5cubwoULmxo1apjPP//cKXU//PBD4+3t7fT/d4mJiaZnz54mNDTUFC5c2FSsWNFMnjz5tl9Ddbu67777rilVqpTx8PAwpUuXNq+99todbY938js+N9tKfmCmMlNvJq/mKjOVmXq72sxUZqoVZqrt/xoGAAAAAAAFGJ+BBwAAAADAAgjwAAAAAABYAAEeAAAAAAALIMADAAAAAGABBHgAAAAAACyAAA8AAAAAgAUQ4AEAAAAAsAACPAAAAAAAFkCAB/6PzWbT559/7rR6DRs2VFRUlNPqOVvPnj3Vpk0bV7dxR44ePSqbzab4+HhXt3JHunXrprFjx7q6DZe7cRuoU6eOli9f7rqGAOQbZmrBxUy1JmYqriHAo0BKSkrSgAEDVK5cOXl5eSksLEwtW7bUt99+m2ePmZiYqBYtWuRZ/YLm3Xff1bx58+zXnfHH0bhx41SnTh35+voqMDBQbdq0UUJCQu4atZjdu3fr66+/1oABA1zdSoEzatQovfrqq8rMzHR1K8B9hZma95ipeYOZmj1m6v2LAI8C5+jRo4qIiNB3332nCRMmaM+ePVq1apUaNWqkfv365biuMUZXrlzJsjw9PV2SFBwcLC8vrxzXd7bs+nUWPz8/PfDAA06tGRsbq379+mnLli2KiYnRlStX1KxZM124cMGpj1OQzZgxQ+3atZOvr6+rW9Hly5dd3YKDZ599VqmpqVq9erWrWwHuG8zUq5ip1sRMzR4z9T5mgAKmRYsW5sEHHzTnz5/PcltKSooxxpgjR44YSWbXrl0Ot0ky69atM8YYs27dOiPJrFq1ykRERBgPDw/z3XffmcjISNOvXz8zaNAgExAQYBo0aGCMMUaSWbFihb3eiRMnTIcOHUzx4sVNkSJFTEREhNmyZYsxxpgePXqY1q1bO/Q2cOBAExkZab8eGRlpBg4caL/+ySefmIiICFO0aFETFBRkOnXqZH7//Xf77dn1e6Nr6117LYwxZteuXUaSOXLkiDHGmLlz5xo/Pz+zatUqU6lSJePj42OaN29ufvvtN/t9rn8OPXr0MJIcLkeOHDGnT582nTt3NiVKlDCFCxc25cuXNx9//HGWnrKTnJxsJJnY2Fj7svDwcPP222+bF154wRQtWtSEhYWZDz/80OF+W7duNTVr1jReXl4mIiLCLF++PMvP+0bvv/++KV++vPHy8jKBgYHmv/7rv+y3ZWZmmnfeeceULVvWFC5c2FSvXt0sXbo0y2u6atUqU7NmTVO4cGHTqFEj8/vvv5tvvvnGVKpUyfj6+pqOHTuaCxcuZNtDRkaGeeCBB8xXX33lsPxOf/ZfffWVqV69uvHy8jKPPvqo2b17t32daz/TFStWmAoVKhgvLy/TpEkTc/z4cfs6o0ePNjVq1DBz5swxZcuWNTabzWRmZppjx46ZVq1aGR8fH+Pr62vatWtnkpKSjDHG/PTTT0aSOXDggEPPkydPNuHh4SYzM9MYY8y+fftMixYtjI+PjwkMDDRdu3Y1//nPf+zrnz9/3nTr1s34+PiY4OBgM2nSpCzbgDHG9OzZ03Tr1i3b1xCAczFTmanMVGYq7i3sgUeBcvr0aa1atUr9+vWTj49Plttz8u72sGHDNG7cOB04cEDVq1eXJM2fP1/u7u7atGmTPvzwwyz3OX/+vCIjI/Xbb79p5cqV+vHHHzVs2LBcHaaUnp6uN998Uz/++KM+//xzHTlyRD179ryjfnPi4sWLmjRpkj755BN9//33On78uIYOHXrTdd99913VrVtXL730khITE5WYmKiwsDCNGjVK+/fv17///W8dOHBAs2bNUokSJe64h9TUVEmSv7+/w/LJkyerdu3a2rVrl/r27at//OMf+umnnyRJFy5c0HPPPaeKFSsqLi5OY8aMybbva3bs2KFXXnlFb7zxhhISErRq1So1aNDAfvtrr72muXPnatasWdq3b58GDRqkrl27KjY21qHOmDFjNGPGDG3evFknTpxQ+/btNW3aNC1atEhff/21YmJiNH369Gz72L17t86cOaPatWs7LL/Tn310dLQmTZqk7du3KzAwUK1atXJ4x//ixYt6++23NX/+fG3atElnz55Vx44dHWr8/PPP+n//7/9p2bJl9s83tmnTRqdPn1ZsbKxiYmJ0+PBhdejQQZJUsWJFRURE6NNPP3Wos2jRInXu3Fk2m02JiYmKjIxUzZo1tWPHDq1atUq///672rdv79D7unXrtGLFCq1Zs0br169XXFxcluf46KOPasOGDdm+hgCch5nKTGWmMlNxD3L1OwjA9bZu3WokmeXLl99yvbvZW/D555873DcyMtLUrFkzS01dt7fgww8/NL6+vubUqVM3ffyc7C240bZt24wkc+7cuVv2e6M73Vsgyfz888/2dd5//30TFBSU7XO4Wb8tW7Y0L7zwwi37yU5mZqZp2bKlqV+/vsPy8PBw07VrV4f1AgMDzaxZs4wxV197f39/h3flZ82adcu9BcuWLTPFihUzZ8+ezXLb+fPnTeHChc3mzZsdlvfu3dt06tTJGPPXa7p27Vr77ePGjTOSzOHDh+3L+vTpY5o3b57tc16xYoVxc3Ozv8Oenex+9osXL7avc+rUKePt7W2WLFlijPnrZ3ptj5Uxxhw4cMBIMlu3bjXGXN1b4OHhYZKTk+3rrFmzxri5uTnsVdi3b5+RZLZt22aMMWbKlCmmXLly9tsTEhKMJLNv3z5jjDGjRo0yzZo1c3gOJ06cMJJMQkKCOXfunPH09Lxp/zf+n/riiy9MoUKFTEZGxi1fIwC5x0xlpjJTr2Km4l7CHngUKMYYSVfPXussN75zm92y68XHx6tWrVpZ3uXOjV27dql169YKDw+Xr6+vGjZsKEk6fvz4XfV2p4oUKaK//e1v9ushISFKTk6+qxr/+Mc/tHjxYtWsWVPDhg3T5s2b7/i+/fv31+7du/XZZ59lue36vSA2m03BwcH23g4cOKAaNWqoSJEi9nXq1q17y8dq2rSpwsPDVa5cOXXr1k2ffvqpLl68KEnav3+//vzzTzVt2lRFixa1XxYsWKDDhw9n21dQUJCKFCmicuXKOSy71Wt46dIleXl5Zfn/e6c/++ufp7+/vypWrKgDBw7Yl7m7uzv8/6hUqZIeeOABh3XCw8NVsmRJ+/UDBw4oLCxMYWFh9mWVK1d2uF/Hjh117NgxbdmyRZL06aefqmbNmqpcubIkKS4uTuvWrXN4/SpVqiRJOnz4sA4fPqz09PSb9n8jb29vZWZmKi0tLdvXEYBzMFOZqczUq5ipuJcQ4FGgVKhQQTabzeGX580UKnT1v+61P06k7E8ucrPDBm+27Hre3t63ffzrH/tWjy9dPYStWbNmKlq0qBYuXKjt27drxYoVkv464c+d9nanz93Dw8Phus1my9Lz7bRo0ULHjh1TVFSUfvvtNzVu3Pi2h95J0oABA7Ry5UqtW7dOpUqVuqPerh1Kebc9SpKvr6927typzz77TCEhIXr99ddVo0YNnTlzxl7366+/Vnx8vP2yf/9+/etf/8q2L5vNdss+b6ZEiRK6ePGiw8/0bn72N3PjHy43+0P8+mU3/v8xxtz0PtcvDwkJUaNGjbRo0SJJ0meffaauXbva183MzFTLli0dXr/4+HgdOnRIDRo0uKuf2enTp1WkSJHbbmMAco+ZykxlpjpipuJeQIBHgeLv76/mzZvr/fffv+lZVs+cOSNJ9ndDExMT7bc58/tMq1evrvj4eJ0+ffqmt5csWdLhsW/3+D/99JP++OMPjR8/Xk8++aQqVap01+/cX//YkvOfu6enpzIyMm76eD179tTChQs1bdo0zZ49O9saxhj1799fy5cv13fffaeyZcvedR+VK1fWjz/+qEuXLtmXXXsX+1bc3d3VpEkTTZgwQbt379bRo0f13XffqXLlyvLy8tLx48dVvnx5h8v176A7Q82aNSVd3UNxzd387K9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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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weVZQt27d8P7772PkyJE4ceIEunfvDjs7O6SlpeHo0aNo1aoVPvzwwzLrCA0NxdatWxEcHIxp06bh+eefx+PHjxEXF4f+/fujR48eeOONN/Ddd9/hpZdewvjx4/H8889DLpfj77//xqFDhxAcHIxBgwbpFXvhXsfGjRvh4OAAGxsbeHl5wdfXF02aNMG0adMghICzszN+/PFHxMbG6tQxb9489OvXD71798b48eNRUFCAZcuWwd7eHnfv3jVaPy1evBiBgYHo0aMHJk+eDGtra6xduxbnzp3Dli1byvXbnT706YPSPPfcc3jzzTfx2WefwdLSEj179sT58+fx2WefoVatWlqXI8ybNw+xsbHo2rUrxo0bh2bNmiEnJwfXrl3D3r17sX79ejRo0AAODg7w9PTEDz/8gF69esHZ2RkuLi6lXpLj7OyMmTNnYtasWXB2dpZukhAWFob33nvvqdd49u/fH/Pnz8ecOXPg7++PS5cuYd68efDy8tL5AisPCwsLzJ8/H++99x4GDRqE0aNH4/79+wgLC6vQYUJjxlsZY7pVq1aIjo7GunXr4OfnBwsLizKvh7x165b0O93Zs2cBPDk3ovBaV39//6e2w8XFBT179sSsWbNgZ2eHtWvX4o8//tC6XKXQmDFjsHTpUpw8eRJffvllufqpLNVl+ylThU85qsaedgZZUV999ZXo1KmTsLOzE0qlUjRp0kS888474sSJE9I8/v7+4rnnnitx+Xv37onx48eLhg0bCrlcLurVqyf69esn/vjjD2ketVotli9fLtq0aSNsbGyEvb298PX1FSEhIeLKlSvSfJ6enqJfv3466/D39xf+/v5aZeHh4cLLy0tYWloKAGLTpk1CCCEuXLggAgMDhYODg3BychJDhgwR169fL/FMtR07dohWrVoJa2tr0bBhQ7FkyRIxbtw44eTkZFA/lebIkSOiZ8+e0rKdO3cWP/74o9Y8xjzbtrx9UHi24K1bt3TqzcnJERMnThT16tUTNjY2onPnziIxMVHUqlVLTJgwQWveW7duiXHjxgkvLy8hl8uFs7Oz8PPzEzNmzNC61OTnn38W7dq1EwqFQuesw9KsWrVK+Pj4SJ/RnDlzdM4yLUlubq6YPHmyqF+/vrCxsRHt27cXO3fuFMOHD9c6e7Ssfi9pzHz55ZfC29tbWFtbCx8fH/HVV1/p1FkeKHa2rTHiFcL4Y/ru3bti8ODBonbt2kImk+lc8lJc4VmwJb2Kb8Nl9cvatWtFkyZNhFwuF76+vuK7774rdZmAgADh7OysddlbeVX37ackMiGE0D/lEpVOrVajbdu2qF+/PmJiYkwdjtlJSEhAt27d8N1332Ho0KGmDofKobqP6YyMDHh6euLjjz/G0qVLTR1Omcxl++FhW6qwUaNGITAwEG5ubkhPT8f69etx8eJFrFq1ytShmVxsbCwSExPh5+cHpVKJ33//HUuWLIG3tzdeeeUVU4dHpagpY/rvv//GX3/9hWXLlsHCwgLjx483dUhazHr7MWh/laiIIUOGiPr16wtra2thZ2cnXnjhBbFv3z5Th2UWfvnlF9GtWzfh5OQkrKyshEqlEsOHDxc3b940dWhUhpoypufMmSNkMpnw8vLSulGCuTDn7YeHbYmIiPTEOwwRERHpicmTiIhIT0yeREREeuLZtnhyp5qbN2/CwcHB6BfbExHRs0EIgYcPH8Ld3f2pz1Rl8sST+6MacqsxIiKqfm7cuKF1C8OSMHnifzdVv3HjBhwdHat8/Wq1GjExMQgKCir1qQDVXU3vg5refoB9ALAPTN3+zMxMeHh4lOtBG0ye+N9NjB0dHU2WPG1tbeHo6FgjNxiAfVDT2w+wDwD2gbm0vzw/3/GEISIiIj0xeRIREemJyZOIiEhP/M2znIQQyM/PR0FBgdHrVqvVsLKyQk5OTqXUXx6WlpawsrLipTpEROVg0uQZHx+PZcuW4eTJk0hLS8OOHTu0nmwvhMDcuXOxceNG3Lt3D506dcKaNWvw3HPPSfPk5uZi8uTJ2LJlCx4/foxevXph7dq1Tz3NWB95eXlIS0tDdna20eosSggBlUqFGzdumDR52draws3NDdbW1iaLgYjoWWDS5Pno0SO0adMGI0eOxKuvvqrz/tKlS7FixQpERETAx8cHCxYsQGBgIC5duiSdShwaGooff/wRUVFRqFOnDiZNmoT+/fvj5MmTsLS0rHCMGo0GycnJsLS0hLu7O6ytrY2e4DQaDbKysmBvb//UC3MrgxACeXl5uHXrFpKTk+Ht7W2SOIiInhUmTZ59+/ZF3759S3xPCIHw8HDMmDFDem7b5s2b4erqisjISISEhODBgwf4v//7P3zzzTd48cUXAQDffvstPDw88PPPP6N3794VjjEvLw8ajQYeHh6wtbWtcH0l0Wg0yMvLg42NjcmSllKphFwuR0pKihQLERGVzGx/80xOTkZ6ejqCgoKkMoVCAX9/fyQkJCAkJAQnT56EWq3Wmsfd3R0tW7ZEQkJCqckzNzcXubm50nRmZiaAJ789qtVqrXnVajUKn9qm0WiM1r6iCusXQlTaOvSJRa1WG2WvXR+F/V68/2uKmt5+gH0AsA9M3X591mu2yTM9PR0A4OrqqlXu6uqKlJQUaR5ra2s4OTnpzFO4fEkWL16MuXPn6pTHxMTo7F1aWVlBpVIhKysLeXl5BrWlvB4+fFip9T9NXl4eHj9+jPj4eOTn55skhtjYWJOs11zU9PYD7AOAfWCq9utzXovZJs9CxX9fFEI89TfHp80zffp0TJw4UZouvCVTUFCQzh2GcnJycOPGDdjb21faoczCmxGb+sb0OTk5UCqV6N69e5UftlWr1YiNjUVgYGCNvbNKbGwsAs+Og1yTY3hF0/82XlBVrKaPAYB9YJTtoALbQOFRyPIw2+SpUqkAPNm7dHNzk8ozMjKkvVGVSoW8vDzcu3dPa+8zIyMDXbt2LbVuhUIBhUKhUy6Xy3UGbEFBAWQyGSwsLCrt98jCQ7WF6zEVCwsLyGSyEvuhqphy3eZArsmpWPKsBn1X08cAwD6o0HZQgX7Tp8/N9pRKLy8vqFQqrd33vLw8xMXFSYnRz88Pcrlca560tDScO3euzORpbDKZrMzXiBEjqiyW4ho1aoTw8HCTrZ+IqDoy6Z5nVlYWrl69Kk0nJycjKSkJzs7OaNiwIUJDQ7Fo0SJ4e3vD29sbixYtgq2tLYYOHQoAqFWrFkaNGoVJkyahTp06cHZ2xuTJk9GqVSvp7NuqkJaWJv1/69atmD17Ni5duiSVKZVKverLy8vjtZZERGbMpHueJ06cQLt27dCuXTsAwMSJE9GuXTvMnj0bADBlyhSEhoZizJgx6NChA1JTUxETE6P1uJiVK1di4MCBeO2119CtWzfY2trixx9/rNKzRVUqlfSqVasWZDKZNC2Xy/HBBx+gQYMGsLW1RatWrbBlyxat5Xv27IlPPvkEkyZNgouLCwIDAwEAu3btgre3N5RKJXr06IHNmzdDJpPh/v370rIJCQno3r07lEolPDw8MG7cODx69AgAEBAQgJSUFEyYMEHaCyYiooozafIMCAiAEELnFRERAeDJ4dCwsDCkpaUhJycHcXFxaNmypVYdNjY2+OKLL3Dnzh1kZ2fjxx9/NKsHW+fk5MDPzw+7d+/GuXPn8P777+Ptt9/Gr7/+qjVfVFQUrKyscOzYMWzYsAHXrl3D4MGDMXDgQCQlJSEkJAQzZszQWubs2bPo3bs3XnnlFZw5cwZbt27F0aNH8dFHHwEAoqOj0aBBA8ybNw9paWlae8hERGQ4sz1hqLqoX78+Jk+eLE1//PHH2L9/P7Zt24ZOnTpJ5V5eXvj000+lE4amTZuGZs2aYdmyZQCAZs2a4dy5c1i4cKG0zLJlyzB06FCEhoYCALy9vfH555/D398f69atg7OzMywtLeHg4CCdgEVERBXH5FnJCgoKsGTJEmzduhWpqanSDRrs7Oy05is8dF3o0qVL6Nixo1bZ888/rzV98uRJXL16Fd99951UVnijheTkZDRv3tzIrSEiIoDJs9J99tlnWLlyJcLDw9GqVSvY2dkhNDRU54YLxW/OUNK1qoV3Iiqk0WgQEhKCcePG6ay3YcOGRmoBEREVx+RZyY4cOYLg4GC89dZbAJ4kvCtXrjx1r9DX1xd79+7VKjtx4oTWdPv27XH+/Hk0bdq01Hqsra1N9pgzIqLqymyv86wumjZtitjYWCQkJODixYsICQkp89aBhUJCQvDHH39g6tSpuHz5Mv773/9qnUgFAFOnTkViYiLGjh2LpKQkXLlyBbt27cLHH38s1dOoUSPEx8cjNTUVt2/frpQ2EhHVNEyelWzWrFlo3749evfujYCAAKhUKq1nlpbGy8sL33//PaKjo9G6dWusW7dOOtu28O5IrVu3RlxcHK5cuYIXXngB7dq1w6xZs7TuyDRv3jxcu3YNTZo0Qd26dSuljURENQ0P2xrZiBEjtO4o5OzsjJ07d5a5zMGDB0u8p+LLL7+Ml19+WZpeuHAhGjRooHXf2Y4dOyImJqbUujt37ozff/+9/A0gIqKnYvI0Y2vXrkXHjh1Rp04dHDt2DMuWLZOu4SQiItNh8jRjV65cwYIFC3D37l00bNgQkyZNwvTp000dFhFRjcfkacZWrlyJlStXmjoMIiIqhicMERER6YnJk4iISE9MnkRERHpi8iQiItITkycREZGemDyJiIj0xEtVDNRo2p4qXd+1Jf2qdH1EJrO4AaDJMWzZsAfGjYWoFNzzrObWrl0LLy8v2NjYwM/PD0eOHDF1SEREzzwmz2ps69atCA0NxYwZM3D69Gm88MIL6Nu3L65fv27q0IiInmlMntXYihUrMGrUKLz33nto3rw5wsPD4eHhgXXr1pk6NCKiZxqTZzWVl5eHkydPIigoSKs8KCgICQkJJoqKiKh6YPKspm7fvo2CggK4urpqlbu6upbrYdxERFQ6Js9qTiaTaU0LIXTKiIhIP0ye1ZSLiwssLS119jIzMjJ09kaJiEg/TJ7VlLW1Nfz8/BAbG6tVHhsbi65du5ooKiKi6oE3SajGJk6ciLfffhsdOnRAly5dsHHjRly/fh0ffPCBqUMjInqmMXkayJh3/NFoNMjMzISjoyMsLIx3MOD111/HnTt3MG/ePKSlpaFly5bYu3cvPD09jbYOIqKaiMmzmhszZgzGjBlj6jCIiKoV/uZJRESkJ+55knnhTcGJ6BnAPU8iIiI9MXkSERHpicmTiIhIT2adPPPz8zFz5kx4eXlBqVSicePGmDdvHjQajTSPEAJhYWFwd3eHUqlEQEAAzp8/b8KoiYioujPr5Pnpp59i/fr1WL16NS5evIilS5di2bJl+OKLL6R5li5dihUrVmD16tU4fvw4VCoVAgMD8fDhQxNGTkRE1ZlZJ8/ExEQEBwejX79+aNSoEQYPHoygoCCcOHECwJO9zvDwcMyYMQOvvPIKWrZsic2bNyM7OxuRkZEmjp6IiKors75U5V//+hfWr1+Py5cvw8fHB7///juOHj2K8PBwAEBycjLS09O1nlmpUCjg7++PhIQEhISElFhvbm4ucnNzpenMzEwAgFqthlqt1ppXrVZDCAGNRqN1uNiYhBDSv5W1jvLQaDQQQkCtVsPS0rJK113Y72oLm4pUYqRoqp5R2v+kIiNEYxo1fQwARfrgGW+HoUw9BvTpd5ko/OY2Q0II/Pvf/8ann34KS0tLFBQUYOHChZg+fToAICEhAd26dUNqairc3d2l5d5//32kpKTgp59+KrHesLAwzJ07V6c8MjIStra2WmVWVlZQqVTw8PCAtbW1VF47vGpvcXc/NKXS15GXl4cbN24gPT0d+fn5lb4+IiJzkp2djaFDh+LBgwdwdHQsc16z3vPcunUrvv32W0RGRuK5555DUlISQkND4e7ujuHDh0vz6fvMyunTp2PixInSdGZmJjw8PBAUFKTTYTk5Obhx4wbs7e1hY1PBvYIKeNoHWZL4+HgsX74cp06dQlpaGrZv346BAweWOn9OTg6USiW6d+9e5W1Vq9WIjY1F4NlxkBt6k4Tpfxs3qCpklPYD7INnuP1AkT4IDIRcLjd1OFXO1GOg8ChkeZh18vzkk08wbdo0vPHGGwCAVq1aISUlBYsXL8bw4cOhUqkAAOnp6XBzc5OWe9ozKxUKBRQKhU65XC7XGbAFBQWQyWSwsLAw6k3b9WXIuh8/foy2bdvi3XffxauvvvrUNlhYWEAmk5XYD1VFrskxfKOpBl82FWo/wD6oBu0HSv4uqklMNQb06XOzTp7Z2dk6X/aWlpbS74JeXl5QqVSIjY1Fu3btADw59BgXF4dPP/20yuM1N3379kXfvn1NHQYRUbVj1slzwIABWLhwIRo2bIjnnnsOp0+fxooVK/Duu+8CeHK4NjQ0FIsWLYK3tze8vb2xaNEi2NraYujQoSaOnoiIqiuzTp5ffPEFZs2ahTFjxiAjIwPu7u4ICQnB7NmzpXmmTJmCx48fY8yYMbh37x46deqEmJgYODg4mDByIiKqzsw6eTo4OCA8PFy6NKUkMpkMYWFhCAsLq7K4iIioZjPrmyQQERGZIyZPIiIiPZn1YVuqmKysLFy9elWaTk5ORlJSEpydndGwYUMTRkZE9Gxj8jRU2AOjVaXRaJCZmQlHR0ejXkt64sQJ9OjRQ5ouvDHE8OHDERERYbT1EBHVNEye1VhAQADM+O6LRETPLP7mSUREpCcmTyIiIj0xeRIREemJyZOIiEhPTJ7lVBNOvKkJbSQiMgYmz6cofERNdna2iSOpfIVtrMmPQiIiKg9eqvIUlpaWqF27NjIyMgAAtra2ZT5o2xAajQZ5eXnIyckxyTNDhRDIzs5GRkYGateuDUtLyyqPgYjoWcLkWQ6FD90uTKDGJoTA48ePoVQqjZ6Y9VG7dm2prUREVDomz3KQyWRwc3NDvXr1oFarjV6/Wq1GfHw8unfvbrJDpnK5nHucRETlxOSpB0tLy0pJMJaWlsjPz4fNyqaQa3IMq8SItwskIqKy8YQhIiIiPTF5EhER6YnJk4iISE8GJ88///wTM2fOxJtvvimdhbp//36cP3/eaMERERGZI4OSZ1xcHFq1aoVff/0V0dHRyMrKAgCcOXMGc+bMMWqARERE5sag5Dlt2jQsWLAAsbGxsLa2lsp79OiBxMREowVHRERkjgxKnmfPnsWgQYN0yuvWrYs7d+5UOCgiIiJzZtB1nrVr10ZaWhq8vLy0yk+fPo369esbJTAiohprcQOA13ybNYP2PIcOHYqpU6ciPT0dMpkMGo0Gx44dw+TJk/HOO+8YO0YiIiKzYlDyXLhwIRo2bIj69esjKysLLVq0QPfu3dG1a1fMnDnT2DESERGZFYMO28rlcnz33XeYN28eTp8+DY1Gg3bt2sHb29vY8REREZmdCt3btkmTJmjSpImxYiEiInomGJQ8J06cWGK5TCaDjY0NmjZtiuDgYDg7O1coOCIiInNkUPI8ffo0Tp06hYKCAjRr1gxCCFy5cgWWlpbw9fXF2rVrMWnSJBw9ehQtWrQwdsxEREQmZdAJQ8HBwXjxxRdx8+ZNnDx5EqdOnUJqaioCAwPx5ptvIjU1Fd27d8eECROMHS8REZHJGZQ8ly1bhvnz58PR0VEqc3R0RFhYGJYuXQpbW1vMnj0bJ0+eNFqgRERE5sKg5PngwQPpZvBF3bp1C5mZmQCe3EghLy+vYtERERGZIYMP27777rvYsWMH/v77b6SmpmLHjh0YNWoUBg4cCAD47bff4OPjY8xYiYiIzIJByXPDhg3o1asX3njjDXh6eqJhw4Z444030KtXL6xfvx4A4Ovriy+//LLCAaampuKtt95CnTp1YGtri7Zt22odDhZCICwsDO7u7lAqlQgICOBj0YiIqFIZdLatvb09/vOf/2DlypX466+/IIRAkyZNYG9vL83Ttm3bCgd37949dOvWDT169MC+fftQr149/Pnnn6hdu7Y0z9KlS7FixQpERETAx8cHCxYsQGBgIC5dugQHB4cKx0BERFRchW6SYG9vj9atWxsrFh2ffvopPDw8sGnTJqmsUaNG0v+FEAgPD8eMGTPwyiuvAAA2b94MV1dXREZGIiQkpNJiIyKimsvg5Hn8+HFs27YN169f1zkxKDo6usKBAcCuXbvQu3dvDBkyBHFxcahfvz7GjBmD0aNHAwCSk5ORnp6OoKAgaRmFQgF/f38kJCSUmjxzc3ORm5srTRee5KRWq6FWq40Suz4K16m2sKlIJUaKxjRqeh8Ypf1PKjJCNKZR08cAwD4wdfv1+f6XCSGEviuIiorCO++8g6CgIMTGxiIoKAhXrlxBeno6Bg0apLWnWBE2Nk86cOLEiRgyZAh+++03hIaGYsOGDXjnnXeQkJCAbt26ITU1Fe7u7tJy77//PlJSUvDTTz+VWG9YWBjmzp2rUx4ZGQlbW1ujxE5ERM+W7OxsDB06FA8ePNC6FLMkBiXP1q1bIyQkBGPHjoWDgwN+//13eHl5ISQkBG5ubiUmJkNYW1ujQ4cOSEhIkMrGjRuH48ePIzExUUqeN2/ehJubmzTP6NGjcePGDezfv7/Eekva8/Tw8MDt27ef2mGVQa1WIzY2FoFnx0Fu6DP8pv9t3KCqWE3vA6O0H2AfPMPtB9gHpm5/ZmYmXFxcypU8DTps++eff6Jfv34AnhwmffToEWQyGSZMmICePXsaLXm6ubnp3N6vefPm2L59OwBApVIBANLT07WSZ0ZGBlxdXUutV6FQQKFQ6JTL5XLI5XJjhG4QuSbH8AFjwriNqab3QYXaD7APqkH7AfaBqdqvz/e/QZeqODs74+HDhwCA+vXr49y5cwCA+/fvIzs725AqS9StWzdcunRJq+zy5cvw9PQEAHh5eUGlUiE2NlZ6Py8vD3FxcejatavR4iAiIirKoD3PF154AbGxsWjVqhVee+01jB8/HgcPHkRsbCx69epltOAmTJiArl27YtGiRXjttdfw22+/YePGjdi4cSOAJ09xCQ0NxaJFi+Dt7Q1vb28sWrQItra2GDp0qNHiICIiKsqg5Ll69Wrk5DzZpZ4+fTrkcjmOHj2KV155BbNmzTJacB07dsSOHTswffp0zJs3D15eXggPD8ewYcOkeaZMmYLHjx9jzJgxuHfvHjp16oSYmBhe40lERJXGoORZ9DmdFhYWmDJlCqZMmWK0oIrq378/+vfvX+r7MpkMYWFhCAsLq5T1ExERFWfQb56WlpYl3hj+zp07sLS0rHBQRERE5syg5Fna1S25ubmwtrauUEBERETmTq/Dtp9//jmAJ4dKv/zyS6172RYUFCA+Ph6+vr7GjZCIiMjM6JU8V65cCeDJnuf69eu1DtFaW1ujUaNG0lNViIiIqiu9kmdycjIAoEePHoiOjoaTk1OlBEVERGTODDrb9tChQ8aOg4iI6JlhUPIsKChAREQEDhw4gIyMDGg0Gq33Dx48aJTgiIiIzJFByXP8+PGIiIhAv3790LJlS8hkMmPHRUREZLYMSp5RUVH473//i5deesnY8RAREZk9g67ztLa2RtOmTY0dCxER0TPBoOQ5adIkrFq1qtSbJRAREVVnBh22PXr0KA4dOoR9+/bhueee03kGWnR0tFGCIyIiMkcGJc/atWtj0KBBxo6FiIjomWBQ8ty0aZOx4yAiInpmGPSbJwDk5+fj559/xoYNG/Dw4UMAwM2bN5GVlWW04IiIiMyRQXueKSkp6NOnD65fv47c3FwEBgbCwcEBS5cuRU5ODu9vS0RE1ZpBe57jx49Hhw4dcO/ePSiVSql80KBBOHDggNGCIyIiMkcGn2177NgxnWd3enp6IjU11SiBERERmSuD9jw1Gg0KCgp0yv/++284ODhUOCgiIiJzZlDyDAwMRHh4uDQtk8mQlZWFOXPm8JZ9RERU7Rl02HblypXo0aMHWrRogZycHAwdOhRXrlyBi4sLtmzZYuwYiYiIzIpBydPd3R1JSUmIiorCyZMnodFoMGrUKAwbNkzrBCIiIqLqyKDkCQBKpRIjR47EyJEjjRkPERGR2TPoN8/Fixfjq6++0in/6quv8Omnn1Y4KCIiInNmUPLcsGEDfH19dcqfe+453iCBiIiqPYOSZ3p6Otzc3HTK69ati7S0tAoHRUREZM4MSp4eHh44duyYTvmxY8fg7u5e4aCIiIjMmUEnDL333nsIDQ2FWq1Gz549AQAHDhzAlClTMGnSJKMGSEREZG4MSp5TpkzB3bt3MWbMGOTl5QEAbGxsMHXqVEyfPt2oARIREZkbvZNnQUEBjh49iqlTp2LWrFm4ePEilEolvL29oVAoKiNGIiIis6J38rS0tETv3r1x8eJFeHl5oWPHjpURFxERkdky6IShVq1a4a+//jJ2LERERM8Eg5LnwoULMXnyZOzevRtpaWnIzMzUehEREVVnBiXPPn364Pfff8fLL7+MBg0awMnJCU5OTqhduzacnJyMHaNk8eLFkMlkCA0NlcqEEAgLC4O7uzuUSiUCAgJw/vz5SouBiIjIoLNtDx06ZOw4nur48ePYuHEjWrdurVW+dOlSrFixAhEREfDx8cGCBQsQGBiIS5cu8dmiRERUKQxKnv7+/saOo0xZWVkYNmwY/vOf/2DBggVSuRAC4eHhmDFjBl555RUAwObNm+Hq6orIyEiEhIRUaZxERFQzGPxUlSNHjmDDhg3466+/sG3bNtSvXx/ffPMNvLy88K9//cuYMWLs2LHo168fXnzxRa3kmZycjPT0dAQFBUllCoUC/v7+SEhIKDV55ubmIjc3V5ou/J1WrVZDrVYbNfbyKFyn2sKmIpUYKRrTqOl9YJT2P6nICNGYRk0fAwD7wNTt1+f736DkuX37drz99tsYNmwYTp06JSWihw8fYtGiRdi7d68h1ZYoKioKp06dwvHjx3XeS09PBwC4urpqlbu6uiIlJaXUOhcvXoy5c+fqlMfExMDW1raCERsuttXnhi9sxD43pZreBxVqP8A+qAbtB9gHpmp/dnZ2uec1KHkuWLAA69evxzvvvIOoqCipvGvXrpg3b54hVZboxo0bGD9+PGJiYmBjU/pfIjKZTGtaCKFTVtT06dMxceJEaTozMxMeHh4ICgqCo6NjxQPXk1qtRmxsLALPjoNck2NYJdP/Nm5QVaym94FR2g+wD57h9gPsA1O3X5+rRQxKnpcuXUL37t11yh0dHXH//n1DqizRyZMnkZGRAT8/P6msoKAA8fHxWL16NS5dugRA9ykvGRkZOnujRSkUihLvhiSXyyGXy40Wv77kmhzDB4wJ4zammt4HFWo/wD6oBu0H2Aemar8+3/8GXari5uaGq1ev6pQfPXoUjRs3NqTKEvXq1Qtnz55FUlKS9OrQoQOGDRuGpKQkNG7cGCqVCrGxsdIyeXl5iIuLQ9euXY0WBxERUVEG7XmGhIRg/Pjx+OqrryCTyXDz5k0kJiZi8uTJmD17ttGCc3BwQMuWLbXK7OzsUKdOHak8NDQUixYtgre3N7y9vbFo0SLY2tpi6NChRouDiIioKIOfqpKZmYkePXogJycH3bt3h0KhwOTJk/HRRx8ZO8anxvL48WOMGTMG9+7dQ6dOnRATE8NrPImIqNLolTyzs7PxySefYOfOnVCr1RgwYID0/M4WLVrA3t6+UoIs6vDhw1rTMpkMYWFhCAsLq/R1ExERAXomzzlz5iAiIgLDhg2DUqlEZGQkNBoNtm3bVlnxERERmR29kmd0dDT+7//+D2+88QYAYNiwYejWrRsKCgpgaWlZKQESERGZG73Otr1x4wZeeOEFafr555+HlZUVbt68afTAiIiIzJVeybOgoADW1tZaZVZWVsjPzzdqUEREROZMr8O2QgiMGDFC6wYDOTk5+OCDD2BnZyeVRUdHGy9CIiIiM6NX8hw+fLhO2VtvvWW0YIiIiJ4FeiXPTZs2VVYcREREzwyDH0lGRETaGk3bU6HlFZYCS583UjBUqQy6ty0REVFNxuRJRESkJyZPIiIiPTF5EhER6YnJk4iISE9MnkRERHpi8iQiItITkycREZGeeJMEI6nIxdG8MJqI6NnCPU8iIiI9MXkSERHpicmTiIhIT0yeREREemLyJCIi0hOTJxERkZ6YPImIiPTE5ElERKQnJk8iIiI9MXkSERHpicmTiIhIT0yeREREemLyJCIi0hOTJxERkZ6YPImIiPTE5ElERKQns06eixcvRseOHeHg4IB69eph4MCBuHTpktY8QgiEhYXB3d0dSqUSAQEBOH/+vIkiJiKimsCsk2dcXBzGjh2LX375BbGxscjPz0dQUBAePXokzbN06VKsWLECq1evxvHjx6FSqRAYGIiHDx+aMHIiIqrOrEwdQFn279+vNb1p0ybUq1cPJ0+eRPfu3SGEQHh4OGbMmIFXXnkFALB582a4uroiMjISISEhpgibiIiqObNOnsU9ePAAAODs7AwASE5ORnp6OoKCgqR5FAoF/P39kZCQUGryzM3NRW5urjSdmZkJAFCr1VCr1QbFprAUBi0HAAqLJ8uqLWwMrgMGxm0uCvu9pvaBUdr/pCIjRGMa1WEMVOR7AOB3ganHgD7f/zIhRMU+7SoihEBwcDDu3buHI0eOAAASEhLQrVs3pKamwt3dXZr3/fffR0pKCn766acS6woLC8PcuXN1yiMjI2Fra1s5DSAiIrOWnZ2NoUOH4sGDB3B0dCxz3mdmz/Ojjz7CmTNncPToUZ33ZDKZ1rQQQqesqOnTp2PixInSdGZmJjw8PBAUFPTUDitNy7CSE3V5KCwE5nfQIPDsOMg1OYZVMv1vg9dvDtRqNWJjY2tsHxil/QD7wMTtr8j3AMDvAlOPgcKjkOXxTCTPjz/+GLt27UJ8fDwaNGgglatUKgBAeno63NzcpPKMjAy4urqWWp9CoYBCodApl8vlkMvlBsWYW1B6si4vuSbH8AFjYNzmpqb3QYXaD7APTNx+Y3wPAM92HxiDqdqvz/e/WZ9tK4TARx99hOjoaBw8eBBeXl5a73t5eUGlUiE2NlYqy8vLQ1xcHLp27VrV4RIRUQ1h1nueY8eORWRkJH744Qc4ODggPT0dAFCrVi0olUrIZDKEhoZi0aJF8Pb2hre3NxYtWgRbW1sMHTrUxNETEVF1ZdbJc926dQCAgIAArfJNmzZhxIgRAIApU6bg8ePHGDNmDO7du4dOnTohJiYGDg4OVRwtERHVFGadPMtzIrBMJkNYWBjCwsIqPyAiIiKYefKkZ0ujaXsMXlZhKbD0eSMGQ0RUicz6hCEiIiJzxORJRESkJyZPIiIiPTF5EhER6YnJk4iISE9MnkRERHpi8iQiItITkycREZGemDyJiIj0xORJRESkJyZPIiIiPTF5EhER6YnJk4iISE98qgoRGQ2frEM1Bfc8iYiI9MTkSUREpCcmTyIiIj0xeRIREemJyZOIiEhPTJ5ERER6YvIkIiLSE5MnERGRnpg8iYiI9MTkSUREpCcmTyIiIj0xeRIREemJyZOIiEhPTJ5ERER6YvIkIiLSE5MnERGRnpg8iYiI9MTkSUREpKdqkzzXrl0LLy8v2NjYwM/PD0eOHDF1SEREVE1Vi+S5detWhIaGYsaMGTh9+jReeOEF9O3bF9evXzd1aEREVA1Vi+S5YsUKjBo1Cu+99x6aN2+O8PBweHh4YN26daYOjYiIqiErUwdQUXl5eTh58iSmTZumVR4UFISEhIQSl8nNzUVubq40/eDBAwDA3bt3oVarDYrDKv+RQcsBgJVGIDtbgzt51pBrNIZVcueOwes3FvaB4dRqNbKzsyvWfsDkfVDTx0BF2g9Ujz6oCKNsBxVo/8OHDwEAQoinzyyecampqQKAOHbsmFb5woULhY+PT4nLzJkzRwDgiy+++OKLL53XjRs3npp7nvk9z0IymUxrWgihU1Zo+vTpmDhxojSt0Whw9+5d1KlTp9RlKlNmZiY8PDxw48YNODo6Vvn6zUFN74Oa3n6AfQCwD0zdfiEEHj58CHd396fO+8wnTxcXF1haWiI9PV2rPCMjA66uriUuo1AooFAotMpq165dWSGWm6OjY43cYIqq6X1Q09sPsA8A9oEp21+rVq1yzffMnzBkbW0NPz8/xMbGapXHxsaia9euJoqKiIiqs2d+zxMAJk6ciLfffhsdOnRAly5dsHHjRly/fh0ffPCBqUMjIqJqqFokz9dffx137tzBvHnzkJaWhpYtW2Lv3r3w9PQ0dWjlolAoMGfOHJ1DyTVJTe+Dmt5+gH0AsA+epfbLhCjPOblERERU6Jn/zZOIiKiqMXkSERHpicmTiIhIT0yeREREemLyNAM1+XFq8fHxGDBgANzd3SGTybBz505Th1Sl1q1bh9atW0sXhXfp0gX79u0zdVgms3jxYshkMoSGhpo6lEqzePFidOzYEQ4ODqhXrx4GDhyIS5cuac0zYsQIyGQyrVfnzp1NFLFxPW3MR0dHo3fv3nBxcYFMJkNSUpLpgi0Dk6eJ1fTHqT169Aht2rTB6tWrTR2KSTRo0ABLlizBiRMncOLECfTs2RPBwcE4f/68qUOrcsePH8fGjRvRunVrU4dSqeLi4jB27Fj88ssviI2NRX5+PoKCgvDokfZN5fv06YO0tDTptXfvXhNFbFxPG/OPHj1Ct27dsGTJEhNH+hQVvjM7Vcjzzz8vPvjgA60yX19fMW3aNBNFZDoAxI4dO0wdhsk5OTmJL7/80tRhVKmHDx8Kb29vERsbK/z9/cX48eNNHVKVycjIEABEXFycVDZ8+HARHBxsuqCqWEljPjk5WQAQp0+fNk1QT8E9TxMqfJxaUFCQVnlZj1Oj6qugoABRUVF49OgRunTpYupwqtTYsWPRr18/vPjii6YOpcoVPhLR2dlZq/zw4cOoV68efHx8MHr0aGRkZJgivEr1LI/5anGHoWfV7du3UVBQoHMDe1dXV50b3VP1dfbsWXTp0gU5OTmwt7fHjh070KJFC1OHVWWioqJw6tQpHD9+3NShVDkhBCZOnIh//etfaNmypVTet29fDBkyBJ6enkhOTsasWbPQs2dPnDx58pm4+87TVIcxz+RpBvR5nBpVP82aNUNSUhLu37+P7du3Y/jw4YiLi3vmvkwMcePGDYwfPx4xMTGwsbExdThV7qOPPsKZM2dw9OhRrfLXX39d+n/Lli3RoUMHeHp6Ys+ePXjllVeqOkyjqw5jnsnThAx5nBpVP9bW1mjatCkAoEOHDjh+/DhWrVqFDRs2mDiyynfy5ElkZGTAz89PKisoKEB8fDxWr16N3NxcWFpamjDCyvPxxx9j165diI+PR4MGDcqc183NDZ6enrhy5UoVRVe5qsOY52+eJsTHqVFJhBDIzc01dRhVolevXjh79iySkpKkV4cOHTBs2DAkJSVVy8QphMBHH32E6OhoHDx4EF5eXk9d5s6dO7hx4wbc3NyqIMKq9yyOee55mlhNf5xaVlYWrl69Kk0nJycjKSkJzs7OaNiwoQkjqxr//ve/0bdvX3h4eODhw4eIiorC4cOHsX//flOHViUcHBy0fusDADs7O9SpU0envLoYO3YsIiMj8cMPP8DBwUE68lSrVi0olUpkZWUhLCwMr776Ktzc3HDt2jX8+9//houLCwYNGmTi6CvuaWP+7t27uH79Om7evAkA0jWwKpUKKpXKZHHrMOm5viSEEGLNmjXC09NTWFtbi/bt22udsl7dHTp0SADQeQ0fPtzUoVWJd999V/rs69atK3r16iViYmJMHZZJVfdLVUoa7wDEpk2bhBBCZGdni6CgIFG3bl0hl8tFw4YNxfDhw8X169dNG7iRPG3Mb9q0qcT+mTNnjumCLgEfSUZERKQn/uZJRESkJyZPIiIiPTF5EhER6YnJk4iISE9MnkRERHpi8iQiItITkycREZGemDyJiIj0xORJJiWTybBz506j1RcQEIDQ0FCj1WdsI0aMwMCBA00dxjPv7bffxqJFi6p0nWFhYWjbtm2VrKuqx/Hq1avx8ssvV9n6qgMmT5Kkp6fj448/RuPGjaFQKODh4YEBAwbgwIEDlbbOtLQ09O3bt9LqNzerVq1CRESENG2ML8n4+HgMGDAA7u7uBv0xYu5/cBR35swZ7NmzBx9//LGpQ6mww4cPQyaT4f79+1rl0dHRmD9/fpXFMXr0aBw/flzn0WhUOiZPAgBcu3YNfn5+OHjwIJYuXYqzZ89i//796NGjB8aOHWtwvUII5Ofn65Tn5eUBeHKzZ3N6uG9p8RpLrVq1ULt2baPW+ejRI7Rp0warV682ar3mavXq1RgyZAgcHBxMHUqlcXZ2rtL2KRQKDB06FF988UWVrfOZZ9pb65K56Nu3r6hfv77IysrSee/evXtCCCGSk5MFAHH69Gmt9wCIQ4cOCSH+d6P3/fv3Cz8/PyGXy8XBgweFv7+/GDt2rJgwYYKoU6eO6N69uxDiyU2yd+zYIdV348YN8frrrwsnJydha2sr/Pz8xC+//CKEEGL48OEiODhYK7bx48cLf39/abr4TcW/+eYb4efnJ+zt7YWrq6t48803xT///CO9X1q8xRXOV9gXQghx+vRpAUAkJycLIZ7c0LpWrVpi//79wtfXV9jZ2YnevXuLmzdvSssUbcPw4cN1bn6dnJws7t69K4YOHSpcXFyEjY2NaNq0qfjqq690YipJ8f4stGbNGtG0aVOhUChEvXr1xKuvvlpmDCUprQ4hhNBoNOLTTz8VXl5ewsbGRrRu3Vps27atxH5u27atsLGxET169BD//POP2Lt3r/D19RUODg7ijTfeEI8ePSq1fQUFBaJ27dpi9+7dWuW5ubnik08+Ee7u7sLW1lY8//zz0pi8f/++sLGxEfv27dNaZvv27cLW1lY8fPhQCCHElClThLe3t1AqlcLLy0vMnDlT5OXlSfPPmTNHtGnTRpou6Qb2wcHBWg81KGv8FW5PRV+Fyxav++7du+Ltt98WtWvXFkqlUvTp00dcvnxZer88Y+/QoUOiY8eOwtbWVtSqVUt07dpVXLt2TXr/8OHDwtraWmRnZ5fa//Q/fCQZ4e7du9i/fz8WLlwIOzs7nfcN2VOaMmUKli9fjsaNG0vLb968GR9++CGOHTsGUcLzCLKysuDv74/69etj165dUKlUOHXqFDQajd7rL5SXl4f58+ejWbNmyMjIwIQJEzBixAjs3bv3qfEaIjs7G8uXL8c333wDCwsLvPXWW5g8eTK+++47nXlXrVqFy5cvo2XLlpg3bx4AoG7duhg/fjwuXLiAffv2wcXFBVevXsXjx48NjunEiRMYN24cvvnmG3Tt2hV3797FkSNHyoxBnzoAYObMmYiOjsa6devg7e2N+Ph4vPXWW6hbty78/f2l+cLCwrB69WrY2tritddew2uvvQaFQoHIyEhkZWVh0KBB+OKLLzB16tQS23LmzBncv38fHTp00CofOXIkrl27hqioKLi7u2PHjh3o06cPzp49C29vb/Tr1w/fffcd+vTpIy0TGRmJ4OBg2NvbA3jyeLSIiAi4u7vj7NmzGD16NBwcHDBlyhQDe77s8efh4YHt27fj1VdfxaVLl+Do6AilUlliPSNGjMCVK1ewa9cuODo6YurUqXjppZdw4cIFyOVyAGWPvfz8fAwcOBCjR4/Gli1bkJeXh99++w0ymUxaR4cOHaBWq/Hbb79pfWZUClNnbzK9X3/9VQAQ0dHRZc6nz57nzp07tZb19/cXbdu21akTRfaUNmzYIBwcHMSdO3dKXL8he57F/fbbbwKAtLdRWrzFlXfPE4C4evWqNM+aNWuEq6trqW0oKd4BAwaIkSNHlhlPaVDCnuf27duFo6OjyMzMLHGZ8jwCrKw6srKyhI2NjUhISNAqHzVqlHjzzTeFEP/rv59//ll6f/HixQKA+PPPP6WykJAQ0bt371Lj2LFjh7C0tBQajUYqu3r1qpDJZCI1NVVr3l69eonp06cLIYSIjo4W9vb20l7tgwcPhI2NjdizZ0+p61q6dKnw8/OTpg3Z8yyutPFXdFwVr/vy5csCgDh27Jj0/u3bt4VSqRT//e9/hRBPH3t37twRAMThw4dLjU0IIZycnERERESZ89AT/M2TpL3Aon+FVlTxPYPSyopKSkpCu3bt4OzsbLQ4Tp8+jeDgYHh6esLBwQEBAQEAgOvXr+sVW3nZ2tqiSZMm0rSbmxsyMjL0quPDDz9EVFQU2rZtiylTpiAhIaFCMQUGBsLT0xONGzfG22+/je+++w7Z2dlGq+PChQvIyclBYGAg7O3tpdfXX3+NP//8U6ue1q1bS/93dXWFra0tGjdurFVWVn89fvwYCoVCa6yeOnUKQgj4+PhorT8uLk5af79+/WBlZYVdu3YBALZv3w4HBwcEBQVJ9Xz//ff417/+BZVKBXt7e8yaNUtnnOirvOOvLBcvXoSVlRU6deokldWpUwfNmjXDxYsXpbKyxp6zszNGjBiB3r17Y8CAAVi1ahXS0tJ01qVUKvUeGzUVkyfB29sbMplMa0MsiYXFk+EiihxyVavVJc5b0uHfksqKKu2QVdH1i2KHe0tbP/DkRJqgoCDY29vj22+/xfHjx7Fjxw4A/zthqbyxlbfthYfQCslkshIPUZelb9++SElJQWhoKG7evIlevXph8uTJetVRlIODA06dOoUtW7bAzc0Ns2fPRps2bXTO8DS0jsLD6nv27EFSUpL0unDhAr7//nuteor2j0wmK7G/yjpM7+LiguzsbK3PT6PRwNLSEidPntRa/8WLF7Fq1SoAgLW1NQYPHozIyEgATw7Zvv7667CyevLL1S+//II33ngDffv2xe7du3H69GnMmDFDZ5wU9bTxqM/4K0tp40cIofVHxNPG3qZNm5CYmIiuXbti69at8PHxwS+//KK1zN27d0s8bE+6mDwJzs7O6N27N9asWYNHjx7pvF/4JVu4URX9izUpKclocbRu3RpJSUm4e/duie/XrVtX56/lstb/xx9/4Pbt21iyZAleeOEF+Pr66r0XWHTdgPHbbm1tjYKCghLXN2LECHz77bcIDw/Hxo0bK7QeKysrvPjii1i6dCnOnDmDa9eu4eDBg2XGUN46WrRoAYVCgevXr6Np06ZaLw8PjwrFXVzhdZYXLlyQytq1a4eCggJkZGTorF+lUknzDRs2DPv378f58+dx6NAhDBs2THrv2LFj8PT0xIwZM9ChQwd4e3sjJSWlzFiKj8eCggKcO3dOmi7P+LO2tpaWLU2LFi2Qn5+PX3/9VSq7c+cOLl++jObNm5cZY3Ht2rXD9OnTkZCQgJYtW0p/TADAn3/+iZycHLRr106vOmsqJk8CAKxduxYFBQV4/vnnsX37dly5cgUXL17E559/ji5dugB4smfYuXNnLFmyBBcuXEB8fDxmzpxptBjefPNNqFQqDBw4EMeOHcNff/2F7du3IzExEQDQs2dPnDhxAl9//TWuXLmCOXPmaH1ZFdewYUNYW1vjiy++wF9//YVdu3YZfO1cYSIICwvD5cuXsWfPHnz22WcG1VVUo0aN8Ouvv+LatWu4ffs2NBoNZs+ejR9++AFXr17F+fPnsXv37jK/JLOysqS9LQBITk5GUlKSdGhw9+7d+Pzzz5GUlISUlBR8/fXX0Gg0aNasWakxFFdWHQ4ODpg8eTImTJiAzZs3488//8Tp06exZs0abN68ucJ9VFTdunXRvn17resRfXx8MGzYMLzzzjuIjo5GcnIyjh8/jk8//VTrxDB/f3+4urpi2LBhaNSoETp37iy917RpU1y/fh1RUVH4888/8fnnn0t7iaXp2bMn9uzZgz179uCPP/7AmDFjtPbmyzP+PD09IZPJsHv3bty6dQtZWVk66/H29kZwcDBGjx6No0eP4vfff8dbb72F+vXrIzg4uFz9lpycjOnTpyMxMREpKSmIiYnRSb5HjhxB48aNtQ79UhlM9WMrmZ+bN2+KsWPHCk9PT2FtbS3q168vXn75ZelkICGEuHDhgujcubNQKpWibdu2IiYmpsQThso6AaIoFDvB5dq1a+LVV18Vjo6OwtbWVnTo0EH8+uuv0vuzZ88Wrq6uolatWmLChAnio48+KvOEocjISNGoUSOhUChEly5dxK5du7ROeiot3pIcPXpUtGrVStjY2IgXXnhBbNu2rcRLVYrasWOHKLqZFT9h6NKlS1J/FtY1f/580bx5c6FUKoWzs7MIDg4Wf/31V6lxFbah+KvwxJUjR44If39/4eTkJJRKpWjdurXYunVrmTEU97Q6NBqNWLVqlWjWrJmQy+Wibt26onfv3iIuLq7Ufi6pv4qflFOS9evXi86dO2uV5eXlidmzZ4tGjRoJuVwuVCqVGDRokDhz5ozWfJ988okAIGbPnq1T7yeffCLq1Kkj7O3txeuvvy5WrlypFV/x2PLy8sSHH34onJ2dRb169cTixYt1Thh62vgTQoh58+YJlUolZDLZUy9VqVWrllAqlaJ3794lXqpSVNGxl56eLgYOHCjc3NyEtbW18PT0FLNnzxYFBQXS/EFBQWLx4sUldTmVQCaEnj/IEBGZUE5ODpo1a4aoqCjpqAhVzLlz59CrVy9cvnwZtWrVMnU4zwQetiWiZ4qNjQ2+/vpr3L5929ShVBs3b97E119/zcSpB+55EhER6Yl7nkRERHpi8iQiItITkycREZGemDyJiIj0xORJRESkJyZPIiIiPTF5EhER6YnJk4iISE9MnkRERHr6fy4i4VdU+4BWAAAAAElFTkSuQmCC", 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", 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", 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796Nt27awt7fHtWvXsGfPHgQFBeGDDz4otI6oqCisXr0a3bp1w+jRo/Hcc8/h0aNHiI+Px0svvYR27drh9ddfx/Lly/Hvf/8bQ4YMwXPPPQdra2tcvnwZO3bsQLdu3dCjR49ixZ5ztrB48WI4OjrC1tYWvr6+CAgIQO3atTF69GiICFxcXPDzzz8jLi4uTx2TJk1C586d0bFjRwwZMgTZ2dmYNWsWHBwccOvWLZO10/Tp0xEWFoZ27dphxIgRsLGxwfz583H06FGsXLmySPfKiqM4bVCQ+vXro3fv3vj8889haWmJ9u3b49ixY/j8889RqVIlgy75kyZNQlxcHFq1aoWPPvoIdevWRXp6Oi5cuIAtW7Zg4cKFqFGjBhwdHeHt7Y2ffvoJHTp0gIuLC1xdXQt8LMXFxQWffPIJxo0bBxcXF+UB/+joaLzzzjtPfIbxpZdewuTJkzFhwgSEhIQgKSkJkyZNgq+vb56DU1FYWFhg8uTJeOedd9CjRw+8++67uHPnDqKjo0t06c6U8T6NbTooKAjr1q3DggULEBwcDAsLi0Kf97tx44ZyX+zIkSMAHvdFyHmWMyQk5Inr4erqivbt22PcuHGwt7fH/PnzcfLkSYNHNnJERkZi5syZOHDgAL755psitVNhysv+U6gSd995Rj2pp5W+7777Tlq0aCH29vai0Wikdu3a8tZbb8n+/fuVaUJCQqR+/fr5zn/79m0ZMmSI1KxZU6ytrcXNzU06d+4sJ0+eVKbRarXy2WefSaNGjcTW1lYcHBwkICBAIiIi5PTp08p03t7e0rlz5zzLCAkJkZCQEIOymJgY8fX1FUtLSwEgS5YsERGR48ePS1hYmDg6OkrlypWlV69ecvHixXx7dK1fv16CgoLExsZGatasKTNmzJCPPvpIKleubFQ7FWT37t3Svn17Zd6WLVvKzz//bDCNKXulFrUNcnrV3bhxI0+96enpMmzYMHFzcxNbW1tp2bKl7N27VypVqiRDhw41mPbGjRvy0Ucfia+vr1hbW4uLi4sEBwfL2LFjDR63+O2336RJkyaiVqvz9M4ryJw5c8Tf31/5jCZMmJCnN2Z+MjIyZMSIEVK9enWxtbWVpk2byoYNG6Rfv34GvSwLa/f8tplvvvlG/Pz8xMbGRvz9/eW7777LU2dRIFevVFPEK2L6bfrWrVvSs2dPcXZ2FpVKleexj9xyeovm95d7Hy6sXebPny+1a9cWa2trCQgIkOXLlxc4T2hoqLi4uBg8+lVU5X3/yY9KRKT46ZQqKq1Wi8aNG6N69eqIjY01dzhlTkJCAlq3bo3ly5ejT58+5g6HiqC8b9Opqanw9vbG4MGDMXPmTHOHU6iysv/wUioV6u2330ZYWBiqVauGlJQULFy4ECdOnMCcOXPMHZrZxcXFYe/evQgODoZGo8Hff/+NGTNmwM/PDy+//LK5w6MCVJRt+vLlyzh37hxmzZoFCwsLDBkyxNwhGSjT+49R55lUYfTq1UuqV68uNjY2Ym9vL23atJFffvnF3GGVCX/88Ye0bt1aKleuLFZWVuLh4SH9+vWTq1evmjs0KkRF2aYnTJggKpVKfH19DR7yLyvK8v7DS6lERER6Kvybb4iIiPQxMRIREelhYiQiItJT7nul6nQ6XL16FY6OjiZ/UJyIiJ4dIoL79+/D09Oz0N/FLPeJ8erVq0a93oqIiMqnS5cuGbw6L7dynxhzXsB96dIlODk5mTmaikmr1SI2Nhbh4eEFviGfqLzjfmB+9+7dg5eX1xN/mKHcJ8acy6dOTk5MjGai1WphZ2cHJycnHhCowuJ+UHY86bYaO98QERHpYWIkIiLSw8RIRESkp9zfYywKEUFWVhays7PNHcpTYWlpCSsrKz6uQkRUBBU+MWZmZuLatWtIS0szdyhPlZ2dHapVq6b8ajkREeWvQidGnU6H8+fPw9LSEp6enrCxsSl3Z1UigszMTNy4cQPnz5+Hn59foQ+2EhFVdBU6MWZmZkKn08HLywt2dnbmDuep0Wg0sLa2RnJyMjIzM2Fra2vukIiIyiyeOgAV4gyqIqwjEZEpVOgzRipl02sAunRzR2Gc6LvmjoCISglPI4iIiPQwMRIREelhYnwClUpV6F///v3NFpuPjw9iYmLMtnwiovKI9xif4Nq1a8r/q1evxvjx45GUlKSUaTSaYtWXmZnJZwmJiMownjE+gYeHh/JXqVIlqFQqZdja2hrvv/8+atSoATs7OwQFBWHlypUG84eGhuLDDz/EsGHD4OrqirCwMADAxo0b4efnB41Gg3bt2mHZsmVQqVS4c+eOMm9CQgLatm0LjUYDLy8vfPTRR3j48KFSb3JyMoYOHaqcvRIRUckxMZZAeno6goODsWnTJhw9ehTvvfce3nzzTfz5558G0y1btgxWVlb4/fffsWjRIly4cAE9e/ZE9+7dkZiYiIiICIwdO9ZgniNHjqBjx454+eWXcfjwYaxevRp79uzBhx9+CABYt24datSogUmTJuHatWsGZ7ZERGQ8XkotgerVq2PEiBHK8ODBg7F161asWbMGLVq0UMrr1KmDmTNnKsOjR49G3bp1MWvWLABA3bp1cfToUUydOlWZZtasWejTpw+ioqIAAH5+fvjyyy8REhKCBQsWwMXFBZaWlnB0dISHh8dTXlMiooqDibEEsrOzMWPGDKxevRpXrlxBRkYGMjIyYG9vbzBds2bNDIaTkpLQvHlzg7LnnnvOYPjAgQM4c+YMli9frpSJiPIau3r16pl4bYiICGBiLJHPP/8cX3zxBWJiYhAUFAR7e3tERUUhMzPTYLrciVJE8twTFBGDYZ1Oh4iICHz00Ud5lluzZk0TrQEREeXGxFgCu3fvRrdu3fDGG28AeJzMTp8+/cSzuYCAAGzZssWgbP/+/QbDTZs2xbFjx1CnTp0C67GxsSm3P5VFRGQu7HxTAnXq1EFcXBwSEhJw4sQJREREICUl5YnzRURE4OTJkxg1ahROnTqF//3vf1i6dCkAKGeSo0aNwt69ezFo0CAkJibi9OnT2LhxIwYPHqzU4+Pjg127duHKlSu4efPmU1lHIqKKhomxBMaNG4emTZuiY8eOCA0NhYeHB7p37/7E+Xx9ffHjjz9i3bp1aNiwIRYsWKD0SlWr1QCAhg0bIj4+HqdPn0abNm3QpEkTjBs3DtWqVVPqmTRpEi5cuIDatWujatWqT2UdiYgqGrNeSs3KykJ0dDSWL1+OlJQUVKtWDf3798cnn3yi/BqEiGDixIlYvHgxbt++jRYtWmDevHmoX79+qcfbv39/gzfduLi4YMOGDYXOs3PnznzLu3btiq5duyrDU6dORY0aNQx+Eqp58+aIjY0tsO6WLVvi77//LlLsRERUNGZNjJ9++ikWLlyIZcuWoX79+ti/fz8GDBiASpUqYciQIQCAmTNnYvbs2Vi6dCn8/f0xZcoUhIWFISkpCY6OjuYMv0Tmz5+P5s2bo0qVKvj9998xa9Ys5RlFIiIyH7Mmxr1796Jbt27o3LkzgMf3zFauXKl0RBERxMTEYOzYsXj55ZcBPH5Y3t3dHStWrEBERITZYi+p06dPY8qUKbh16xZq1qyJ4cOHY8yYMeYOi4iowjNrYvzXv/6FhQsX4tSpU/D398fff/+NPXv2KC/GPn/+PFJSUhAeHq7Mo1arERISgoSEhHwTY86zhDnu3bsHANBqtdBqtQbTarVa5dlAnU73FNawYJ9//jk+//zzPOVPKw6dTgcRgVarhaWl5VNZRkFy2l1rYfuEKcuwXNsOUXEp+wG3JbMpatubNTGOGjUKd+/eRUBAACwtLZGdnY2pU6eid+/eAKD08HR3dzeYz93dHcnJyfnWOX36dEycODFPeWxsLOzs7AzKrKys4OHhgQcPHuR59rC8yczMxKNHj7Br1y5kZWWZJYa4oC/NslyTyPV4DZGx4uLizB1ChZWWllak6cyaGFevXo0ffvgBK1asQP369ZGYmIioqCh4enqiX79+ynT5PQxf0Euzx4wZg2HDhinD9+7dg5eXF8LDw+Hk5GQwbXp6Oi5dugQHBweDTi/lUXp6OjQaDdq2bVvq66rVahEXF4ewIx/BWpdeqss2mTGXzR0BPeOU/SAsDNbW1uYOp0LKuYL4JGZNjB9//DFGjx6N119/HQAQFBSE5ORkTJ8+Hf369VPeAZrTYzVHampqnrPIHGq1WnnkQZ+1tXWejTE7OxsqlQoWFhZKL9jyysLCAiqVKt92KC3WuvRnNzHyQEYmYs59sKIrarubNRukpaXlSUiWlpbKfTZfX194eHgYXHrIzMxEfHw8WrVqVaqxEhFRxWDWM8YuXbpg6tSpqFmzJurXr49Dhw5h9uzZGDhwIIDHl1CjoqIwbdo0+Pn5wc/PD9OmTYOdnR369OljztCJiKicMmti/OqrrzBu3DhERkYiNTUVnp6eiIiIwPjx45VpRo4ciUePHiEyMlJ5wD82NvaZfoaRiIjKLrMmRkdHR8TExCiPZ+RHpVIhOjoa0dHRpRYXAPiM3lxqy7owo3OpLYuIiApXvnuclHPz58+Hr68vbG1tERwcjN27d5s7JCKiZx4T4zNq9erViIqKwtixY3Ho0CG0adMGnTp1wsWLF80dGhHRM42J8Rk1e/ZsvP3223jnnXdQr149xMTEwMvLCwsWLDB3aEREzzQmxmdQZmYmDhw4YPCqPAAIDw9HQkKCmaIiIiofmBifQTdv3kR2dna+r8oryg8lExFRwZgYn2HFeVUeEREVDRPjM8jV1RWWlpZ5zg4Le1UeEREVDRPjM8jGxgbBwcF53tIfFxfHV+UREZWQWR/wJ+MNGzYMb775Jpo1a4bnn38eixcvxsWLF/H++++bOzQiomcaE2MByvrbaF577TX8888/mDRpEq5du4YGDRpgy5Yt8Pb2NndoRETPNCbGZ1hkZCQiIyPNHQYRUbnCe4xERER6mBiJiIj0MDESERHpYWIkIiLSw8RIRESkh4mRiIhIDxMjERGRHiZGIiIiPUyMREREevjmm4JEVyrFZd0tvWUREVGheMb4jNq1axe6dOkCT09PqFQqbNiwwdwhERGVC0yMz6iHDx+iUaNGmDt3rrlDISIqV3gp9RnVqVMndOrUydxhEBGVOzxjJCIi0sPESEREpIeXUomIStP0GoAu3dxRGKeC9KDnGSMREZEeJkYiIiI9vJT6jHrw4AHOnDmjDJ8/fx6JiYlwcXFBzZo1zRgZEdGzjYmxIGX8Wvr+/fvRrl07ZXjYsGEAgH79+mHp0qVmioqI6NnHxPiMCg0NhYiYOwwionKH9xiJiIj0MDESERHpYWIkIiLSw8RIRESkh4kRqBCdWCrCOhIRmUKFTozW1tYAgLS0NDNH8vTlrGPOOhMRUf4q9OMalpaWcHZ2RmpqKgDAzs4OKpXKzFGZloggLS0NqampcHZ2hqWlpblDIiIq0yp0YgQADw8PAFCSY3nl7OysrCsRERXM7InxypUrGDVqFH755Rc8evQI/v7++PbbbxEcHAzg8RnPxIkTsXjxYty+fRstWrTAvHnzUL9+fZMsX6VSoVq1anBzc4NWqzVJnWWNtbU1zxSJiIrIrInx9u3baN26Ndq1a4dffvkFbm5uOHv2LJydnZVpZs6cidmzZ2Pp0qXw9/fHlClTEBYWhqSkJDg6OposFktLSyYPIiIyb2L89NNP4eXlhSVLlihlPj4+yv8igpiYGIwdOxYvv/wyAGDZsmVwd3fHihUrEBERkafOjIwMZGRkKMP37t0DAGi12nJ7RljW5bS71sLWzJGUALcdKiHuB+ZX1BygEjP24w8MDETHjh1x+fJlxMfHo3r16oiMjMS7774LADh37hxq166NgwcPokmTJsp83bp1g7OzM5YtW5anzujoaEycODFP+YoVK2BnZ/f0VoaIiMq0tLQ09OnTB3fv3oWTk1OB05k1MdraPv7mNGzYMPTq1Qt//fUXoqKisGjRIrz11ltISEhA69atceXKFXh6eirzvffee0hOTsavv/6ap878zhi9vLxw8+bNQhuCnh6tVou4uDiEHfkI1s/qL5ePuWzuCOgZx/3A/O7duwdXV9cnJkajL6WePXsWS5YswdmzZzFnzhy4ublh69at8PLyKnLHGJ1Oh2bNmmHatGkAgCZNmuDYsWNYsGAB3nrrLWW63I9QiEiBj1Wo1Wqo1eo85dbW1nyGz8ysdenP7gGB2w6ZCPcD8ylqDjDqAf/4+HgEBQXhzz//xLp16/DgwQMAwOHDhzFhwoQi11OtWjUEBgYalNWrVw8XL14E8H+PUqSkpBhMk5qaCnd3d2NCJyIiKpRRiXH06NGYMmUK4uLiYGNjo5S3a9cOe/fuLXI9rVu3RlJSkkHZqVOn4O3tDQDw9fWFh4cH4uLilPGZmZmIj49Hq1atjAmdiIioUEZdSj1y5AhWrFiRp7xq1ar4559/ilzP0KFD0apVK0ybNg2vvvoq/vrrLyxevBiLFy8G8PgSalRUFKZNmwY/Pz/4+flh2rRpsLOzQ58+fYwJnYiIqFBGJUZnZ2dcu3YNvr6+BuWHDh1C9erVi1xP8+bNsX79eowZMwaTJk2Cr68vYmJi0LdvX2WakSNH4tGjR4iMjFQe8I+NjTXpM4xEREQ5jEqMffr0wahRo7BmzRqoVCrodDr8/vvvGDFihEGnmaJ46aWX8NJLLxU4XqVSITo6GtHR0caESkREVCxG3WOcOnUqatasierVq+PBgwcIDAxE27Zt0apVK3zyySemjpGIiKjUGHXGaG1tjeXLl2PSpEk4dOgQdDodmjRpAj8/P1PHR0REVKpK9Eq42rVro3bt2qaKhYiIyOyMSozDhg3Lt1ylUsHW1hZ16tRBt27d4OLiUqLgiIiISptRifHQoUM4ePAgsrOzUbduXYgITp8+DUtLSwQEBGD+/PkYPnw49uzZk+cBfiIiorLMqM433bp1wwsvvICrV6/iwIEDOHjwIK5cuYKwsDD07t0bV65cQdu2bTF06FBTx0tERPRUGZUYZ82ahcmTJxu8hNXJyQnR0dGYOXMm7OzsMH78eBw4cMBkgRIREZUGoxLj3bt3kZqamqf8xo0byu8fOjs7IzMzs2TRERERlTKjL6UOHDgQ69evx+XLl3HlyhWsX78eb7/9Nrp37w4A+Ouvv+Dv72/KWImIiJ46ozrfLFq0CEOHDsXrr7+OrKysxxVZWaFfv3744osvAAABAQH45ptvTBcpERFRKTAqMTo4OODrr7/GF198gXPnzkFEULt2bTg4OCjTNG7c2FQxEhERlZoSPeDv4OCAhg0bmioWIiIiszM6Me7btw9r1qzBxYsX83SyWbduXYkDIyIiMgejOt+sWrUKrVu3xvHjx7F+/XpotVocP34c27dvR6VKlUwdIxERUakxKjFOmzYNX3zxBTZt2gQbGxvMmTMHJ06cwKuvvoqaNWuaOkYiIqJSY1RiPHv2LDp37gwAUKvVePjwIVQqFYYOHYrFixebNEAiIqLSZFRidHFxwf379wEA1atXx9GjRwEAd+7cQVpamumiIyIiKmVGdb5p06YN4uLiEBQUhFdffRVDhgzB9u3bERcXhw4dOpg6RiIiolJjVGKcO3cu0tPTAQBjxoyBtbU19uzZg5dffhnjxo0zaYBERESlyajEqP87ixYWFhg5ciRGjhxpsqCIiIjMxah7jJaWlvm+RPyff/6BpaVliYMiIiIyF6MSo4jkW56RkQEbG5sSBURERGROxbqU+uWXXwIAVCoVvvnmG4N3o2ZnZ2PXrl0ICAgwbYRERESlqFiJMeeXM0QECxcuNLhsamNjAx8fHyxcuNC0ERIREZWiYiXG8+fPAwDatWuHdevWoXLlyk8lKCIiInMxqlfqjh07TB0HERFRmWBUYszOzsbSpUuxbds2pKamQqfTGYzfvn27SYIjIiIqbUYlxiFDhmDp0qXo3LkzGjRoAJVKZeq4iIiIzMKoxLhq1Sr873//w7///W9Tx0NERGRWRj3HaGNjgzp16pg6FiIiIrMzKjEOHz4cc+bMKfBBfyIiomeVUZdS9+zZgx07duCXX35B/fr1YW1tbTB+3bp1JgmOiIiotBmVGJ2dndGjRw9Tx0JERGR2RiXGJUuWmDoOIiKiMsGoe4wAkJWVhd9++w2LFi3C/fv3AQBXr17FgwcPTBYcERFRaTPqjDE5ORkvvvgiLl68iIyMDISFhcHR0REzZ85Eeno635dKRETPLKPOGIcMGYJmzZrh9u3b0Gg0SnmPHj2wbds2kwVHRERU2ozulfr777/n+e1Fb29vXLlyxSSBERERmYNRZ4w6nQ7Z2dl5yi9fvgxHR8cSB0VERGQuRiXGsLAwxMTEKMMqlQoPHjzAhAkTjH5N3PTp06FSqRAVFaWUiQiio6Ph6ekJjUaD0NBQHDt2zKj6iYiIisKoxPjFF18gPj4egYGBSE9PR58+feDj44MrV67g008/LXZ9+/btw+LFi9GwYUOD8pkzZ2L27NmYO3cu9u3bBw8PD4SFhSm9YImIiEzNqMTo6emJxMREfPzxx4iIiECTJk0wY8YMHDp0CG5ubsWq68GDB+jbty++/vprgx8+FhHExMRg7NixePnll9GgQQMsW7YMaWlpWLFihTFhExERPZFRnW8AQKPRYMCAARgwYECJAhg0aBA6d+6MF154AVOmTFHKz58/j5SUFISHhytlarUaISEhSEhIQERERL71ZWRkICMjQxm+d+8eAECr1UKr1ZYoVjJOTrtrLWzNHEkJcNuhEuJ+YH5FzQFGJcbp06fD3d0dAwcONCj/7rvvcOPGDYwaNapI9axatQoHDx7Evn378oxLSUkBALi7uxuUu7u7Izk5udDYJk6cmKc8NjYWdnZ2RYqLno64oC/NHYLxtmwxdwRUTnA/MJ+0tLQiTWdUYly0aFG+lzPr16+P119/vUiJ8dKlSxgyZAhiY2Nha1vwN6jcP4IsIoX+MPKYMWMwbNgwZfjevXvw8vJCeHg4nJycnhgXmZ5Wq0VcXBzCjnwEa126ucMxzpjL5o6AnnHcD8wv5wrikxiVGFNSUlCtWrU85VWrVsW1a9eKVMeBAweQmpqK4OBgpSw7Oxu7du3C3LlzkZSUlO+yUlNT85xF6lOr1VCr1XnKra2t8/wKCJUua136s3tA4LZDJsL9wHyKmgOM6nzj5eWF33//PU/577//Dk9PzyLV0aFDBxw5cgSJiYnKX7NmzdC3b18kJiaiVq1a8PDwQFxcnDJPZmYm4uPj0apVK2PCJiIieiKjzhjfeecdREVFQavVon379gCAbdu2YeTIkRg+fHiR6nB0dESDBg0Myuzt7VGlShWlPCoqCtOmTYOfnx/8/Pwwbdo02NnZoU+fPsaETURE9ERGJcaRI0fi1q1biIyMRGZmJgDA1tYWo0aNwpgxY0wW3MiRI/Ho0SNERkbi9u3baNGiBWJjY/l2HSIiemqKnRizs7OxZ88ejBo1CuPGjcOJEyeg0Wjg5+eX77294ti5c6fBsEqlQnR0NKKjo0tULxERUVEVOzFaWlqiY8eOOHHiBHx9fdG8efOnERcREZFZGNX5JigoCOfOnTN1LERERGZnVGKcOnUqRowYgU2bNuHatWu4d++ewR8REdGzyqjONy+++CIAoGvXrgYP2+c8fJ/fT1IRERE9C4xKjDt27DB1HERERGWCUYkxJCTE1HEQERGVCUbdYwSA3bt344033kCrVq1w5coVAMB///tf7Nmzx2TBERERlTajEuPatWvRsWNHaDQaHDx4UPmZp/v372PatGkmDZCIiKg0GZUYp0yZgoULF+Lrr782eClrq1atcPDgQZMFR0REVNqMSoxJSUlo27ZtnnInJyfcuXOnpDERERGZjVGdb6pVq4YzZ87Ax8fHoHzPnj2oVauWKeIiIjLgM3qzuUMoEbWlYOZz5o6CisKoM8aIiAgMGTIEf/75J1QqFa5evYrly5djxIgRiIyMNHWMREREpcboX9e4d+8e2rVrh/T0dLRt2xZqtRojRozAhx9+aOoYiYiISk2xEmNaWho+/vhjbNiwAVqtFl26dFF+fzEwMBAODg5PJUgiIqLSUqzEOGHCBCxduhR9+/aFRqPBihUroNPpsGbNmqcVHxERUakqVmJct24dvv32W7z++usAgL59+6J169bIzs6GpaXlUwmQiIioNBWr882lS5fQpk0bZfi5556DlZUVrl69avLAiIiIzKFYiTE7Oxs2NjYGZVZWVsjKyjJpUEREROZSrEupIoL+/ftDrVYrZenp6Xj//fdhb2+vlK1bt850ERIREZWiYiXGfv365Sl74403TBYMERGRuRUrMS5ZsuRpxUFERFQmGP2zU0REROUREyMREZEeJkYiIiI9TIxERER6mBiJiIj0MDESERHpYWIkIiLSw8RIRESkh4mRiIhIDxMjERGRHiZGIiIiPUyMREREepgYiYiI9DAxEhER6WFiJCIi0sPESEREpIeJkYiISA8TIxERkR4mRiIiIj1mTYzTp09H8+bN4ejoCDc3N3Tv3h1JSUkG04gIoqOj4enpCY1Gg9DQUBw7dsxMERMRUXln1sQYHx+PQYMG4Y8//kBcXByysrIQHh6Ohw8fKtPMnDkTs2fPxty5c7Fv3z54eHggLCwM9+/fN2PkRERUXlmZc+Fbt241GF6yZAnc3Nxw4MABtG3bFiKCmJgYjB07Fi+//DIAYNmyZXB3d8eKFSsQERFhjrCJiKgcM2tizO3u3bsAABcXFwDA+fPnkZKSgvDwcGUatVqNkJAQJCQk5JsYMzIykJGRoQzfu3cPAKDVaqHVap9m+FSAnHbXWtiaOZIS4LZjdmpLMXcIJaK2eBw/9wPzKWoOKDOJUUQwbNgw/Otf/0KDBg0AACkpKQAAd3d3g2nd3d2RnJycbz3Tp0/HxIkT85THxsbCzs7OxFFTccQFfWnuEIy3ZYu5I6jwZj5n7ghMg/uB+aSlpRVpujKTGD/88EMcPnwYe/bsyTNOpVIZDItInrIcY8aMwbBhw5The/fuwcvLC+Hh4XBycjJt0FQkWq0WcXFxCDvyEax16eYOxzhjLps7ggqvQfSv5g6hRNQWgsnNdNwPzCjnCuKTlInEOHjwYGzcuBG7du1CjRo1lHIPDw8Aj88cq1WrppSnpqbmOYvMoVaroVar85RbW1vD2traxJFTcVjr0p/dAwK3HbPLyM7/y/CzhvuB+RQ1B5i1V6qI4MMPP8S6deuwfft2+Pr6Goz39fWFh4cH4uLilLLMzEzEx8ejVatWpR0uERFVAGY9Yxw0aBBWrFiBn376CY6Ojso9xUqVKkGj0UClUiEqKgrTpk2Dn58f/Pz8MG3aNNjZ2aFPnz7mDJ2IiMopsybGBQsWAABCQ0MNypcsWYL+/fsDAEaOHIlHjx4hMjISt2/fRosWLRAbGwtHR8dSjpaIiCoCsyZGkSd3v1apVIiOjkZ0dPTTD4iIiCo8viuViIhIDxMjERGRHiZGIiIiPUyMREREepgYiYiI9DAxEhER6WFiJCIi0sPESEREpIeJkYiISA8TIxERkZ4y8bNTVDif0ZvNHUKJqC2l3PzILBGVfzxjJCIi0sPESEREpIeJkYiISA8TIxERkR4mRiIiIj1MjERERHqYGImIiPQwMRIREelhYiQiItLDxEhERKSHiZGIiEgPEyMREZEeJkYiIiI9TIxERER6mBiJiIj0MDESERHpYWIkIiLSw8RIRESkh4mRiIhIDxMjERGRHiZGIiIiPUyMREREepgYiYiI9DAxEhER6WFiJCIi0sPESEREpIeJkYiISA8TIxERkR4mRiIiIj3PRGKcP38+fH19YWtri+DgYOzevdvcIRERUTlV5hPj6tWrERUVhbFjx+LQoUNo06YNOnXqhIsXL5o7NCIiKofKfGKcPXs23n77bbzzzjuoV68eYmJi4OXlhQULFpg7NCIiKoeszB1AYTIzM3HgwAGMHj3aoDw8PBwJCQn5zpORkYGMjAxl+O7duwCAW7duQavVPr1gnyKrrIfmDqFErHSCtDQd/sm0gbVOZ+5wjPPPP+aOoMLjflAGPOP7wf379wEAIlLodGU6Md68eRPZ2dlwd3c3KHd3d0dKSkq+80yfPh0TJ07MU+7r6/tUYqSi6WPuAEpququ5I6BygPtB2XD//n1UqlSpwPFlOjHmUKlUBsMikqcsx5gxYzBs2DBlWKfT4datW6hSpUqB89DTde/ePXh5eeHSpUtwcnIydzhEZsH9wPxEBPfv34enp2eh05XpxOjq6gpLS8s8Z4epqal5ziJzqNVqqNVqgzJnZ+enFSIVg5OTEw8IVOFxPzCvws4Uc5Tpzjc2NjYIDg5GXFycQXlcXBxatWplpqiIiKg8K9NnjAAwbNgwvPnmm2jWrBmef/55LF68GBcvXsT7779v7tCIiKgcKvOJ8bXXXsM///yDSZMm4dq1a2jQoAG2bNkCb29vc4dGRaRWqzFhwoQ8l7iJKhLuB88OlTyp3yoREVEFUqbvMRIREZU2JkYiIiI9TIxERER6mBiJiIj0MDFSqZg+fTpUKhWioqLMHQpRqfHx8YFKpcrzN2jQIHOHRoUo849r0LNv3759WLx4MRo2bGjuUIhK1b59+5Cdna0MHz16FGFhYejVq5cZo6In4RkjPVUPHjxA37598fXXX6Ny5crmDoeoVFWtWhUeHh7K36ZNm1C7dm2EhISYOzQqBBMjPVWDBg1C586d8cILL5g7FCKzyszMxA8//ICBAwfyBw3KOF5Kpadm1apVOHjwIPbt22fuUIjMbsOGDbhz5w769+9v7lDoCZgY6am4dOkShgwZgtjYWNja2po7HCKz+/bbb9GpU6cn/uQRmR9fCUdPxYYNG9CjRw9YWloqZdnZ2VCpVLCwsEBGRobBOKLyLDk5GbVq1cK6devQrVs3c4dDT8AzRnoqOnTogCNHjhiUDRgwAAEBARg1ahSTIlUoS5YsgZubGzp37mzuUKgImBjpqXB0dESDBg0Myuzt7VGlSpU85UTlmU6nw5IlS9CvXz9YWfGQ+yxgr1Qioqfot99+w8WLFzFw4EBzh0JFxHuMREREenjGSEREpIeJkYiISA8TIxERkR4mRiIiIj1MjERERHqYGImIiPQwMRIREelhYiQiItLDxFiBqFQqbNiwwWT1hYaGIioqymT1mVr//v3RvXt3c4fxzHvzzTcxbdo0o+aNjo5G48aNC53mwoULUKlUSExMNGoZzyofHx/ExMQ89eWYo32bN2+OdevWldryTI2J0UxSUlIwePBg1KpVC2q1Gl5eXujSpQu2bdv21JZ57do1dOrU6anVX9bMmTMHS5cuVYZNkch37dqFLl26wNPT06gvGmX9y0Ruhw8fxubNmzF48GCj5h8xYoTBNm3OLysVIQHn175eXl64du1aqb6jeNy4cRg9ejR0Ol2pLdOUmBjN4MKFCwgODsb27dsxc+ZMHDlyBFu3bkW7du0waNAgo+sVEWRlZeUpz8zMBAB4eHhArVYbXb+pFRSvqVSqVAnOzs4mrfPhw4do1KgR5s6da9J6y6q5c+eiV69ecHR0NGp+BwcHVKlSxcRRUXFYWlrCw8OjVF9g3rlzZ9y9exe//vprqS3TpIRKXadOnaR69ery4MGDPONu374tIiLnz58XAHLo0CGDcQBkx44dIiKyY8cOASBbt26V4OBgsba2lu3bt0tISIgMGjRIhg4dKlWqVJG2bduKiAgAWb9+vVLfpUuX5LXXXpPKlSuLnZ2dBAcHyx9//CEiIv369ZNu3boZxDZkyBAJCQlRhkNCQmTIkCHK8H//+18JDg4WBwcHcXd3l969e8v169eV8QXFm1vOdDltISJy6NAhASDnz58XEZElS5ZIpUqVZOvWrRIQECD29vbSsWNHuXr1qjKP/jr069dPABj8nT9/Xm7duiV9+vQRV1dXsbW1lTp16sh3332XJ6b85G7PHPPmzZM6deqIWq0WNzc3eeWVVwqNIT8F1SEiotPp5NNPPxVfX1+xtbWVhg0bypo1a/Jt58aNG4utra20a9dOrl+/Llu2bJGAgABxdHSU119/XR4+fFjg+mVnZ4uzs7Ns2rRJKfvyyy+lQYMGyvD69esFgMydO1cpCw8Pl9GjR4uIyIQJE6RRo0bK/7nXf8eOHcq2vnbtWgkNDRWNRiMNGzaUhIQEg3h+/PFHCQwMFBsbG/H29pbPPvvsiZ9HpUqVZMmSJcp4/T/9bTm3Y8eOSadOncTe3l7c3NzkjTfekBs3boiIyMKFC8XT01Oys7MN5unSpYu89dZbIiJy5swZ6dq1q7i5uYm9vb00a9ZM4uLiDKb39vaWL774QkSKtr9nZWXJwIEDxcfHR2xtbcXf319iYmKU6Z/Uvvp179y5U5o3by42Njbi4eEho0aNEq1Wq4wPCQmRwYMHy8cffyyVK1cWd3d3mTBhgkH8EyZMEC8vL7GxsZFq1arJ4MGDDcb3799f3nzzzQLbuCxjYixl//zzj6hUKpk2bVqh0xUnMTZs2FBiY2PlzJkzcvPmTQkJCREHBwf5+OOP5eTJk3LixAkRMTxw3L9/X2rVqiVt2rSR3bt3y+nTp2X16tXKwciYxPjtt9/Kli1b5OzZs7J3715p2bKldOrUSRlfULy5FTUxWltbywsvvCD79u2TAwcOSL169aRPnz7KPPrrcOfOHXn++efl3XfflWvXrsm1a9ckKytLBg0aJI0bN5Z9+/bJ+fPnJS4uTjZu3FjoZ5MjvwPxvn37xNLSUlasWCEXLlyQgwcPypw5cwqNIbfC6hAR+c9//iMBAQGydetWOXv2rCxZskTUarXs3LnToP1atmwpe/bskYMHD0qdOnUkJCREwsPD5eDBg7Jr1y6pUqWKzJgxo8D1y2nzlJQUpezw4cOiUqmUJBEVFSWurq7Sq1cvERHRarXi4OAgv/zyi4gYJsb79+/Lq6++Ki+++KKy/hkZGcq2HhAQIJs2bZKkpCTp2bOneHt7Kwfr/fv3i4WFhUyaNEmSkpJkyZIlotFolKRX0Oehnxj/+usvASC//fabXLt2Tf7555981/vq1avi6uoqY8aMkRMnTsjBgwclLCxM2rVrJyKP92EbGxv57bfflHlu3bolNjY28uuvv4qISGJioixcuFAOHz4sp06dkrFjx4qtra0kJycr8xQ3MWZmZsr48ePlr7/+knPnzskPP/wgdnZ2snr16iK1b07dly9fFjs7O4mMjJQTJ07I+vXrxdXV1SDxhYSEiJOTk0RHR8upU6dk2bJlolKpJDY2VkRE1qxZI05OTrJlyxZJTk6WP//8UxYvXmzQjvPnzxcfH59827isY2IsZX/++acAkHXr1hU6XXES44YNGwzmDQkJkcaNG+epU//AsWjRInF0dCzw4GBMYswt50B0//79QuPNraiJEYCcOXNGmWbevHni7u5e4DrkF2+XLl1kwIABhcZTkPwOxGvXrhUnJye5d+9evvM8qc2eVMeDBw/E1tY2z9nU22+/Lb179xaR/2s//QP39OnTBYCcPXtWKYuIiJCOHTsWGMf69evF0tJSdDqdUqbT6cTV1VV+/PFHERFp3LixTJ8+Xdzc3EREJCEhQaysrJTPXD8xiuS/XeVs6998841SduzYMQGgfKnr06ePhIWFGcz38ccfS2BgoDL8pMSY3z6Vn3Hjxkl4eLhB2aVLlwSAJCUliYhI165dZeDAgcr4RYsWiYeHR75fdHIEBgbKV199pQwXNzHmJzIy0uBqQmHtm1P3f/7zH6lbt67B5zpv3jxxcHBQzoJDQkLkX//6l0E9zZs3l1GjRomIyOeffy7+/v6SmZlZYGw//fSTWFhY5DmzfhbwHmMpk///K18qlcpkdTZr1qxIZfoSExPRpEkTuLi4mCyOQ4cOoVu3bvD29oajoyNCQ0MBABcvXixWbEVlZ2eH2rVrK8PVqlVDampqser44IMPsGrVKjRu3BgjR45EQkJCiWIKCwuDt7c3atWqhTfffBPLly9HWlqayeo4fvw40tPTERYWBgcHB+Xv+++/x9mzZw3qadiwofK/u7s77OzsUKtWLYOywtrr0aNHUKvVBtuqSqVC27ZtsXPnTty5cwfHjh3D+++/j+zsbJw4cQI7d+5E06ZN4eDgUKx1zh1vtWrVAECJ78SJE2jdurXB9K1bt8bp06eRnZ1d7GUV5sCBA9ixY4dB+wYEBACA0sZ9+/bF2rVrkZGRAQBYvnw5Xn/9dVhaWgJ4fC965MiRCAwMhLOzMxwcHHDy5Mk8+0JxLVy4EM2aNUPVqlXh4OCAr7/+uth1njhxAs8//7zB59q6dWs8ePAAly9fVsr0Pw/AcP/q1asXHj16hFq1auHdd9/F+vXr8/QX0Gg00Ol0Shs9S5gYS5mfnx9UKhVOnDhR6HQWFo8/mpxECgBarTbfae3t7YtUpk+j0Txx+frLLmz5wOMDQXh4OBwcHPDDDz9g3759WL9+PYD/6/xT1NiKuu7W1tYGwyqVKk/MT9KpUyckJycjKioKV69eRYcOHTBixIhi1aHP0dERBw8exMqVK1GtWjWMHz8ejRo1wp07d0xSR04vv82bNyMxMVH5O378OH788UeDevTbR6VS5dtehfUadHV1RVpaWp7PLzQ0FDt37sTu3bvRqFEjODs7o23btoiPj8fOnTuVL0TFlTteAEp8IpLny2Tuzzq/z7+wbbYgOp0OXbp0MWjfxMREnD59Gm3btgUAdOnSBTqdDps3b8alS5ewe/duvPHGG0odH3/8MdauXYupU6di9+7dSExMRFBQUJ62zFGUbf5///sfhg4dioEDByI2NhaJiYkYMGBAgXUWpLC21C8vbHvx8vJCUlIS5s2bB41Gg8jISLRt29Yg5lu3bsHOzu6Jx5qyiImxlLm4uKBjx46YN28eHj58mGd8zgG0atWqAB4/YpHDlN3MGzZsiMTERNy6dSvf8VWrVjVY9pOWf/LkSdy8eRMzZsxAmzZtEBAQUOyzN/1lA6Zfdxsbm3zPLqpWrYr+/fvjhx9+QExMDBYvXlyi5VhZWeGFF17AzJkzcfjwYVy4cAHbt28vNIai1hEYGAi1Wo2LFy+iTp06Bn9eXl4liju3nOcPjx8/blAeGhqKY8eO4ccff1SSYEhICH777TckJCQgJCSkwDqLuv65BQYGYs+ePQZlCQkJ8Pf3V87Scm+zp0+fNjhbt7GxAYAnLr9p06Y4duwYfHx88rRxzpc6jUaDl19+GcuXL8fKlSvh7++P4OBgpY7du3ejf//+6NGjB4KCguDh4YELFy4UuMyibPO7d+9Gq1atEBkZiSZNmqBOnTp5rhIUpX0DAwORkJBgkIQTEhLg6OiI6tWrFzqvPo1Gg65du+LLL7/Ezp07sXfvXhw5ckQZf/ToUTRt2rTI9ZUlTIxmMH/+fGRnZ+O5557D2rVrcfr0aZw4cQJffvklnn/+eQCPN7qWLVtixowZOH78OHbt2oVPPvnEZDH07t0bHh4e6N69O37//XecO3cOa9euxd69ewEA7du3x/79+/H999/j9OnTmDBhAo4ePVpgfTVr1oSNjQ2++uornDt3Dhs3bsTkyZONii3nIB8dHY1Tp05h8+bN+Pzzz42qS5+Pjw/+/PNPXLhwATdv3oROp8P48ePx008/4cyZMzh27Bg2bdqEevXqFVjHgwcPlDMIADh//jwSExOVy1mbNm3Cl19+icTERCQnJ+P777+HTqdD3bp1C4wht8LqcHR0xIgRIzB06FAsW7YMZ8+exaFDhzBv3jwsW7asxG2kr2rVqmjatGmehNSgQQNUqVIFy5cvVxJjaGgoNmzYgEePHuFf//pXgXX6+Pjg8OHDSEpKws2bN4t8Rjd8+HBs27YNkydPxqlTp7Bs2TLMnTvX4Oy+ffv2mDt3Lg4ePIj9+/fj/fffNzjrcXNzg0ajwdatW3H9+nXcvXs332UNGjQIt27dQu/evfHXX3/h3LlziI2NxcCBAw2STt++fbF582Z89913BmeLwONteN26dUhMTMTff/+NPn36FHp2XpT9vU6dOti/fz9+/fVXnDp1CuPGjcO+ffsMpilK+0ZGRuLSpUsYPHgwTp48iZ9++gkTJkzAsGHDlDPXJ1m6dCm+/fZbHD16FOfOncN///tfaDQaeHt7K9Ps3r0b4eHhRaqvzDHPrU26evWqDBo0SLy9vcXGxkaqV68uXbt2NbjRfvz4cWnZsqVoNBpp3LixxMbG5tv5Rr+TikjBHTyQq3PChQsX5JVXXhEnJyexs7OTZs2ayZ9//qmMHz9+vLi7u0ulSpVk6NCh8uGHHxba+WbFihXi4+MjarVann/+edm4caPBTf+C4s3Pnj17JCgoSGxtbaVNmzayZs2afB/X0Jfz6ECO3B0RkpKSlPbMqWvy5MlSr1490Wg04uLiIt26dZNz584VGFfOOuT+69evn4iI7N69W0JCQqRy5crKYwc5vQYLiiG3J9Wh0+lkzpw5UrduXbG2tpaqVatKx44dJT4+vsB2zq+9cneMyc/ChQulZcuWecpfeeUVsbS0lLt37yoxubi4SLNmzQpdRmpqqoSFhYmDg0OhjxPk1/Ek53ENa2trqVmzpsyaNctgWVeuXJHw8HCxt7cXPz8/2bJli0HnGxGRr7/+Wry8vMTCwqLQxzVOnTolPXr0EGdnZ9FoNBIQECBRUVEGHVaysrKkWrVqeTo1iTzu8NKuXTvRaDTi5eUlc+fOzbO/6He+EXny/p6eni79+/eXSpUqibOzs3zwwQcyevRoo9q3KI9r5D6GdOvWTdnO169fLy1atBAnJyext7eXli1bGnT2unz5slhbW8ulS5cKbOOyTCVSzJsyRFRhpKeno27duli1apVyNYPoST7++GPcvXu3xLclzKX0XoVARM8cW1tbfP/997h586a5Q6FniJubW4k6sZkbzxiJiIj0sPMNERGRHiZGIiIiPUyMREREepgYiYiI9DAxEhER6WFiJCIi0sPESEREpIeJkYiISA8TIxERkZ7/B+qlgcDd3OhdAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 1\n", + "\n", + "for col in data.drop(columns=['TargetEncoded']):\n", + "\n", + " # Calculating the percentage of dropouts by col\n", + " result = data.groupby(col)['TargetEncoded'].value_counts(normalize=True).mul(100).unstack()\n", + " result = result.fillna(0)\n", + " plotit = result[result[1] > 65]\n", + " # print(plotit.sort_values(by=1, ascending=False))\n", + "\n", + " if plotit.empty == False:\n", + " # Plotting\n", + " plotit.plot(kind='bar', figsize=(5, 3))\n", + " plt.title('Percentage of Target 0 and Target 1 by ' + column_name)\n", + " plt.xlabel(col)\n", + " plt.ylabel('Percentage')\n", + " plt.xticks(rotation=0)\n", + " plt.grid(True)\n", + " plt.legend(title='Target')\n", + "\n", + " # Show the plot\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "OTr9751MZqOX", + "metadata": { + "id": "OTr9751MZqOX" + }, + "source": [ + "Not all columns plotted are applicable such as Curricular units or any other continuous numeric values.\n", + "\n", + "But some things to note from the above plots:\n", + "\n", + "- for Marital status: 6 - legally separated has a high percentage of dropped compared to not dropped.\n", + "\n", + "- in Application mode below have high percentages for Dropped also\n", + " - 2 - Ordinance No. 612/93,\n", + " - 26 - Ordinance No. 533-A/99, item b2 (Different Plan)\n", + " - 27 - Ordinance No. 533-A/99, item b3 (Other Institution)\n", + "\n", + "- for Course: those taking 33 - Biofuel Production Technologies have a higher likelihood of dropping out.\n", + "- Age of Enrollment: ages 29, 31, 38, 51, 58, 59, 61, 70 mostly the higher the age the chance of dropping out increases\n", + "- For both parents' occupation: 90 - Other Situation, 99 - (blank) are high risk.\n", + "\n", + "Some of these might be anomalies but it's worth pointing out." + ] + }, + { + "cell_type": "markdown", + "id": "lMVrQBuEZ3OA", + "metadata": { + "id": "lMVrQBuEZ3OA" + }, + "source": [ + "# Target Variable Imbalance\n", + "Let's address the imbalance of the target variable that we pointed out earlier. Since the target of Not Dropped Out (0) vs Dropped (1) has a ratio of 70% to 30%, we will use upsampling of the minority class." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "abJd516LaAqt", + "metadata": { + "id": "abJd516LaAqt" + }, + "outputs": [], + "source": [ + "from sklearn.utils import resample\n", + "\n", + "# Separate majority and minority classes\n", + "majority_class = data[data['TargetEncoded'] == 0]\n", + "minority_class = data[data['TargetEncoded'] == 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "y2Kat1OZaItJ", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 313 + }, + "id": "y2Kat1OZaItJ", + "outputId": "352bf8dc-ebde-490f-e3cd-578a8c4454a1" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Marital status Application mode Application order Course Daytime/evening attendance\\t Previous qualification \\\n", + "1 1 15 1 9254 1 1 \n", + "3 1 17 2 9773 1 1 \n", + "4 2 39 1 8014 0 1 \n", + "5 2 39 1 9991 0 19 \n", + "6 1 1 1 9500 1 1 \n", + "\n", + " Previous qualification (grade) Nationality Mothers qualification Fathers qualification Mothers occupation Fathers occupation \\\n", + "1 160.0 1 1 3 3 3 \n", + "3 122.0 1 38 37 5 3 \n", + "4 100.0 1 37 38 9 9 \n", + "5 133.1 1 37 37 9 7 \n", + "6 142.0 1 19 38 7 10 \n", + "\n", + " Admission grade Displaced Educational special needs Debtor Tuition fees up to date Gender Scholarship holder Age at enrollment \\\n", + "1 142.5 1 0 0 0 1 0 19 \n", + "3 119.6 1 0 0 1 0 0 20 \n", + "4 141.5 0 0 0 1 0 0 45 \n", + "5 114.8 0 0 1 1 1 0 50 \n", + "6 128.4 1 0 0 1 0 1 18 \n", + "\n", + " International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) Curricular units 1st sem (evaluations) \\\n", + "1 0 0 6 6 \n", + "3 0 0 6 8 \n", + "4 0 0 6 9 \n", + "5 0 0 5 10 \n", + "6 0 0 7 9 \n", + "\n", + " Curricular units 1st sem (approved) Curricular units 1st sem (grade) Curricular units 1st sem (without evaluations) \\\n", + "1 6 14.000000 0 \n", + "3 6 13.428571 0 \n", + "4 5 12.333333 0 \n", + "5 5 11.857143 0 \n", + "6 7 13.300000 0 \n", + "\n", + " Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) Curricular units 2nd sem (evaluations) \\\n", + "1 0 6 6 \n", + "3 0 6 10 \n", + "4 0 6 6 \n", + "5 0 5 17 \n", + "6 0 8 8 \n", + "\n", + " Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) Curricular units 2nd sem (without evaluations) Unemployment rate \\\n", + "1 6 13.666667 0 13.9 \n", + "3 5 12.400000 0 9.4 \n", + "4 6 13.000000 0 13.9 \n", + "5 5 11.500000 5 16.2 \n", + "6 8 14.345000 0 15.5 \n", + "\n", + " Inflation rate GDP TargetEncoded \n", + "1 -0.3 0.79 0 \n", + "3 -0.8 -3.12 0 \n", + "4 -0.3 0.79 0 \n", + "5 0.3 -0.92 0 \n", + "6 2.8 -4.06 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of Majority Class: (3003, 37)\n", + "Shape of Minority Class: (1421, 37)\n" + ] + } + ], + "source": [ + "display(majority_class.head())\n", + "print('Shape of Majority Class:', majority_class.shape)\n", + "print('Shape of Minority Class:', minority_class.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "kwWEwqd-aU0W", + "metadata": { + "id": "kwWEwqd-aU0W" + }, + "source": [ + "Separate the Dropped (1) vs Not Dropped (0) its own dataframe. Then apply the resample function to create an upsampled minority. Then add the upsample minority with the majority class." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "Qu9oOpJhaZGn", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + }, + "id": "Qu9oOpJhaZGn", + "outputId": "1425883e-2e0a-4a33-db9f-3a3912f5af36" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Marital status Application mode Application order Course Daytime/evening attendance\\t Previous qualification \\\n", + "3564 1 44 1 9119 1 39 \n", + "2760 1 39 1 9500 1 4 \n", + "4015 4 39 1 9991 0 1 \n", + "3577 1 42 1 9500 1 1 \n", + "3501 1 39 1 9070 1 1 \n", + "\n", + " Previous qualification (grade) Nationality Mothers qualification Fathers qualification Mothers occupation Fathers occupation \\\n", + "3564 130.0 1 19 3 3 4 \n", + "2760 150.0 1 3 38 3 7 \n", + "4015 133.1 1 37 37 5 5 \n", + "3577 100.0 1 19 19 9 9 \n", + "3501 140.0 1 37 37 9 10 \n", + "\n", + " Admission grade Displaced Educational special needs Debtor Tuition fees up to date Gender Scholarship holder Age at enrollment \\\n", + "3564 130.0 0 0 1 0 1 0 23 \n", + "2760 127.0 1 0 0 1 1 0 26 \n", + "4015 138.7 0 0 0 1 0 0 31 \n", + "3577 100.0 0 0 1 1 1 0 24 \n", + "3501 118.0 0 0 1 1 1 1 45 \n", + "\n", + " International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) Curricular units 1st sem (evaluations) \\\n", + "3564 0 0 5 5 \n", + "2760 0 2 7 8 \n", + "4015 0 0 5 7 \n", + "3577 0 0 8 16 \n", + "3501 0 0 6 6 \n", + "\n", + " Curricular units 1st sem (approved) Curricular units 1st sem (grade) Curricular units 1st sem (without evaluations) \\\n", + "3564 0 0.000000 0 \n", + "2760 6 13.046667 0 \n", + "4015 3 10.333333 0 \n", + "3577 2 10.250000 1 \n", + "3501 6 13.500000 0 \n", + "\n", + " Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) Curricular units 2nd sem (evaluations) \\\n", + "3564 0 5 5 \n", + "2760 2 8 10 \n", + "4015 0 5 6 \n", + "3577 0 8 16 \n", + "3501 0 6 6 \n", + "\n", + " Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) Curricular units 2nd sem (without evaluations) Unemployment rate \\\n", + "3564 0 0.00000 0 15.5 \n", + "2760 7 13.30625 0 11.1 \n", + "4015 3 12.00000 0 10.8 \n", + "3577 2 10.25000 1 12.7 \n", + "3501 0 0.00000 0 16.2 \n", + "\n", + " Inflation rate GDP TargetEncoded \n", + "3564 2.8 -4.06 1 \n", + "2760 0.6 2.02 1 \n", + "4015 1.4 1.74 1 \n", + "3577 3.7 -1.70 1 \n", + "3501 0.3 -0.92 1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minority Upsampled: (3003, 37)\n" + ] + } + ], + "source": [ + "# Upsample minority class\n", + "minority_upsampled = resample(minority_class,\n", + " replace=True, # Sample with replacement\n", + " n_samples=len(majority_class), # Match number of majority class\n", + " random_state=42) # Reproducible results\n", + "display(minority_upsampled.head())\n", + "print('Minority Upsampled:', minority_upsampled.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "mb-meN0kahgs", + "metadata": { + "id": "mb-meN0kahgs" + }, + "source": [ + "First confirm the TargetEncoded distribution count." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "n-IUVJrBajBp", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 178 + }, + "id": "n-IUVJrBajBp", + "outputId": "b54a7563-ba27-47fb-d0b3-8a00df65c7eb" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TargetEncoded\n", + "0 3003\n", + "1 1421\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['TargetEncoded'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "EPkerI5Papul", + "metadata": { + "id": "EPkerI5Papul" + }, + "source": [ + "Now combine the upsampled minority with the majority population. This will be the new dataset we will work on." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "bminbfvQauGA", + "metadata": { + "id": "bminbfvQauGA" + }, + "outputs": [], + "source": [ + "# Combine majority class with upsampled minority class\n", + "balanced_data = pd.concat([majority_class, minority_upsampled])\n", + "\n", + "# Now 'balanced_data' contains an upsampled distribution of the minority class" + ] + }, + { + "cell_type": "markdown", + "id": "-qZEbQ3IazJJ", + "metadata": { + "id": "-qZEbQ3IazJJ" + }, + "source": [ + "Compare the TargetEncoded value counts with the new unbalanced_data." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "ypXGThRXa0Pm", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 365 + }, + "id": "ypXGThRXa0Pm", + "outputId": "ec34d05b-b26e-4b51-e699-ab3fcbcc0e5c" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Marital statusApplication modeApplication orderCourseDaytime/evening attendance\\tPrevious qualificationPrevious qualification (grade)NationalityMothers qualificationFathers qualificationMothers occupationFathers occupationAdmission gradeDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentInternationalCurricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Unemployment rateInflation rateGDPTargetEncoded
11151925411160.011333142.5100010190066614.0000000066613.666667013.9-0.30.790
31172977311122.01383753119.6100100200068613.42857100610512.40000009.4-0.8-3.120
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" + ], + "text/plain": [ + " Marital status Application mode Application order Course Daytime/evening attendance\\t Previous qualification \\\n", + "1 1 15 1 9254 1 1 \n", + "3 1 17 2 9773 1 1 \n", + "4 2 39 1 8014 0 1 \n", + "5 2 39 1 9991 0 19 \n", + "6 1 1 1 9500 1 1 \n", + "\n", + " Previous qualification (grade) Nationality Mothers qualification Fathers qualification Mothers occupation Fathers occupation \\\n", + "1 160.0 1 1 3 3 3 \n", + "3 122.0 1 38 37 5 3 \n", + "4 100.0 1 37 38 9 9 \n", + "5 133.1 1 37 37 9 7 \n", + "6 142.0 1 19 38 7 10 \n", + "\n", + " Admission grade Displaced Educational special needs Debtor Tuition fees up to date Gender Scholarship holder Age at enrollment \\\n", + "1 142.5 1 0 0 0 1 0 19 \n", + "3 119.6 1 0 0 1 0 0 20 \n", + "4 141.5 0 0 0 1 0 0 45 \n", + "5 114.8 0 0 1 1 1 0 50 \n", + "6 128.4 1 0 0 1 0 1 18 \n", + "\n", + " International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) Curricular units 1st sem (evaluations) \\\n", + "1 0 0 6 6 \n", + "3 0 0 6 8 \n", + "4 0 0 6 9 \n", + "5 0 0 5 10 \n", + "6 0 0 7 9 \n", + "\n", + " Curricular units 1st sem (approved) Curricular units 1st sem (grade) Curricular units 1st sem (without evaluations) \\\n", + "1 6 14.000000 0 \n", + "3 6 13.428571 0 \n", + "4 5 12.333333 0 \n", + "5 5 11.857143 0 \n", + "6 7 13.300000 0 \n", + "\n", + " Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) Curricular units 2nd sem (evaluations) \\\n", + "1 0 6 6 \n", + "3 0 6 10 \n", + "4 0 6 6 \n", + "5 0 5 17 \n", + "6 0 8 8 \n", + "\n", + " Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) Curricular units 2nd sem (without evaluations) Unemployment rate \\\n", + "1 6 13.666667 0 13.9 \n", + "3 5 12.400000 0 9.4 \n", + "4 6 13.000000 0 13.9 \n", + "5 5 11.500000 5 16.2 \n", + "6 8 14.345000 0 15.5 \n", + "\n", + " Inflation rate GDP TargetEncoded \n", + "1 -0.3 0.79 0 \n", + "3 -0.8 -3.12 0 \n", + "4 -0.3 0.79 0 \n", + "5 0.3 -0.92 0 \n", + "6 2.8 -4.06 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of unbalanced_data: (6006, 37)\n", + "Distribution of unbalanced_data: TargetEncoded\n", + "0 3003\n", + "1 3003\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "display(balanced_data.head())\n", + "print('Shape of unbalanced_data:', balanced_data.shape)\n", + "print('Distribution of unbalanced_data:', balanced_data['TargetEncoded'].value_counts())" + ] + }, + { + "cell_type": "markdown", + "id": "a9pF-WLCa9NA", + "metadata": { + "id": "a9pF-WLCa9NA" + }, + "source": [ + "So the new variable with the upsampled data confirms that the distribution of each TargetEncoded value are equal.\n", + "\n", + "Let's plot to confirm visually." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "N1BnSGLFa-5V", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "N1BnSGLFa-5V", + "outputId": "d1a90c7d-6531-4b7e-d761-3126b9cfe0c9" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x='TargetEncoded', data=balanced_data)\n", + "plt.title('Distribution of Updated Target Classes')\n", + "\n", + "# show percentage of each Target value on plot\n", + "total = len(data)\n", + "for p in plt.gca().patches:\n", + " height = p.get_height()\n", + " plt.text(p.get_x() + p.get_width() / 2.,\n", + " height + 0.1,\n", + " '{:.1f}%'.format((height / total) * 100),\n", + " ha=\"center\")\n", + " # Add custom legend labels\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "F5T8VT04bRLR", + "metadata": { + "id": "F5T8VT04bRLR" + }, + "source": [ + "Looks good. Now I'd like to keep name of the main dataframe that we'll be working on to be called *data* too so I'll set the *unbalanced_data* to be the new *data* variable." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "mka1NiKobfQv", + "metadata": { + "id": "mka1NiKobfQv" + }, + "outputs": [], + "source": [ + "data = balanced_data" + ] + }, + { + "cell_type": "markdown", + "id": "vXnC4YxibWFs", + "metadata": { + "id": "vXnC4YxibWFs" + }, + "source": [ + "Confirm by plotting the same TargetEncoded distribution but using *data* variable." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "9bs0356bbZli", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "9bs0356bbZli", + "outputId": "1b5ac2bd-1a06-4ac7-c4f3-2cf33d91a58d" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.countplot(x='TargetEncoded', data=data)\n", + "plt.title('Distribution of Updated Target Classes')\n", + "\n", + "# show percentage of each Target value on plot\n", + "total = len(data)\n", + "for p in plt.gca().patches:\n", + " height = p.get_height()\n", + " plt.text(p.get_x() + p.get_width() / 2.,\n", + " height + 0.1,\n", + " '{:.1f}%'.format((height / total) * 100),\n", + " ha=\"center\")\n", + " # Add custom legend labels\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "HwPiJhG4bpoC", + "metadata": { + "id": "HwPiJhG4bpoC" + }, + "source": [ + "# Binning Categorical Variables\n", + "Using the list of these categorical columns we identified above earlier, we'll go through each one and address appropriately.\n", + "\n", + "- Application mode\n", + "- Application order\n", + "- Course\n", + "- Fathers occupation\n", + "- Mothers occupation\n", + "- Nationality\n", + "- Marital status\n", + "- Previous qualification\n", + "- Fathers qualification\n", + "- Mothers qualification\n", + "\n", + "Let's start with the Qualification columns." + ] + }, + { + "cell_type": "markdown", + "id": "R6N_ieZOb749", + "metadata": { + "id": "R6N_ieZOb749" + }, + "source": [ + "## Qualification Variables\n", + "For example, in the Qualification columns used by *Previous qualification*, *Mothers qualification* and *Fathers qualification*, there are 34 different possible values that can be grouped to similar qualifications. Perhaps group them into 9 levels starting from primary education level all the way up to higher education. From research and to the best of my knowledge, I bunched each of the the 34 values to the most comparable ones.\n", + "\n", + "There would need to be buckets for Vocational and the incomplete ones, Unknown and Nonliterate.\n", + "\n", + "The mapping chart below is the result of the grouping. Using this as a guideline, we'll use the functions .map to accomplish this.\n", + "\n", + "| Original Qualification: 34 Distinct Values | New Bin Value | New (Binned) Qualification Values: 9 Distinct Values and its ID Value |\n", + "|----------------------------------------------------------------------|----------------------|------------------------------------|\n", + "| 1 - Secondary Education - 12th Year of Schooling or Equivalent | Secondary - 7 | Unknown - 1 |\n", + "| 2 - Higher Education - Bachelor's Degree | Higher - 9 | Nonliterate - 2 |\n", + "| 3 - Higher Education - Degree | Higher - 9 | Elementary - 3 |\n", + "| 4 - Higher Education - Master's | Higher - 9 | Middle School Incomplete - 4 |\n", + "| 5 - Higher Education - Doctorate | Higher - 9 | Middle School - 5 |\n", + "| 6 - Frequency of Higher Education | Higher - 9 | Secondary Incomplete - 6 |\n", + "| 9 - 12th Year of Schooling - Not Completed | Secondary Incomplete - 6| Secondary - 7 |\n", + "| 10 - 11th Year of Schooling - Not Completed | Secondary Incomplete - 6| Vocational - 8 |\n", + "| 11 - 7th Year (Old) | Middle School - 5 | Higher - 9 |\n", + "| 12 - Other - 11th Year of Schooling | Secondary - 7 | |\n", + "| 13 - 2nd year complementary high school course | Secondary - 7 | |\n", + "| 14 - 10th Year of Schooling | Secondary - 7 | |\n", + "| 15 - 10th year of schooling - not completed | Secondary Incomplete - 6 ||\n", + "| 18 - General commerce course | Higher - 9 | |\n", + "| 19 - Basic Education 3rd Cycle (9th/10th/11th Year) or Equivalent | Secondary Incomplete - 6| |\n", + "| 20 - Complementary High School Course | Secondary - 7 | |\n", + "| 22 - Technical-professional course | Vocational - 8 | |\n", + "| 25 - Complementary High School Course - not concluded | Secondary Incomplete - 6| |\n", + "| 26 - 7th year of schooling | Middle School - 5 | |\n", + "| 27 - 2nd cycle of the general high school course | Secondary Incomplete - 6| |\n", + "| 29 - 9th Year of Schooling - Not Completed | Secondary Incomplete - 6| |\n", + "| 30 - 8th year of schooling | Middle School Incomplete - 4 | |\n", + "| 31 - General Course of Administration and Commerce | Higher - 9 | |\n", + "| 33 - Supplementary Accounting and Administration | Vocational - 8 | |\n", + "| 34 - Unknown | Unknown - 1 | |\n", + "| 35 - Can't read or write | Nonliterate - 2 | |\n", + "| 36 - Can read without having a 4th year of schooling | Nonliterate - 2 | |\n", + "| 37 - Basic education 1st cycle (4th/5th year) or equivalent | Elementary - 3 | |\n", + "| 38 - Basic Education 2nd Cycle (6th/7th/8th Year) or Equivalent | Middle School - 5 | |\n", + "| 39 - Technological specialization course | Vocational - 8 | |\n", + "| 40 - Higher education - degree (1st cycle) | Secondary - 7 | |\n", + "| 41 - Specialized higher studies course | Higher - 9 | |\n", + "| 42 - Professional higher technical course | Higher - 9 | |\n", + "| 43 - Higher Education - Master (2nd cycle) | Higher - 9 | |\n", + "| 44 - Higher Education - Doctorate (3rd cycle) | Higher - 9 | |\n", + "\n", + "\n", + "I'll store this maping chart above into a variable called *qualification_bin_mapping*." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "CSlYbSsNcRDj", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 624 + }, + "id": "CSlYbSsNcRDj", + "outputId": "724e9437-ece2-4932-c6aa-52a7a21287bf" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 7,\n", + " 2: 9,\n", + " 3: 9,\n", + " 4: 9,\n", + " 5: 9,\n", + " 6: 9,\n", + " 9: 6,\n", + " 10: 6,\n", + " 11: 5,\n", + " 12: 7,\n", + " 13: 7,\n", + " 14: 7,\n", + " 15: 6,\n", + " 18: 9,\n", + " 19: 6,\n", + " 20: 7,\n", + " 22: 8,\n", + " 25: 6,\n", + " 26: 5,\n", + " 27: 6,\n", + " 29: 6,\n", + " 30: 4,\n", + " 31: 9,\n", + " 33: 8,\n", + " 34: 1,\n", + " 35: 2,\n", + " 36: 2,\n", + " 37: 3,\n", + " 38: 5,\n", + " 39: 8,\n", + " 40: 7,\n", + " 41: 9,\n", + " 42: 9,\n", + " 43: 9,\n", + " 44: 9}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "qualification_bin_mapping = {\n", + " 1:7,\n", + " 2:9,\n", + " 3:9,\n", + " 4:9,\n", + " 5:9,\n", + " 6:9,\n", + " 9:6,\n", + " 10:6,\n", + " 11:5,\n", + " 12:7,\n", + " 13:7,\n", + " 14:7,\n", + " 15:6,\n", + " 18:9,\n", + " 19:6,\n", + " 20:7,\n", + " 22:8,\n", + " 25:6,\n", + " 26:5,\n", + " 27:6,\n", + " 29:6,\n", + " 30:4,\n", + " 31:9,\n", + " 33:8,\n", + " 34:1,\n", + " 35:2,\n", + " 36:2,\n", + " 37:3,\n", + " 38:5,\n", + " 39:8,\n", + " 40:7,\n", + " 41:9,\n", + " 42:9,\n", + " 43:9,\n", + " 44:9\n", + "}\n", + "\n", + "display(qualification_bin_mapping)" + ] + }, + { + "cell_type": "markdown", + "id": "zcQrp9RZcWlE", + "metadata": { + "id": "zcQrp9RZcWlE" + }, + "source": [ + "From the above output, we're assured the chart is implemented and can go ahead and map the 3 columns: Previous qualification, Mothers qualification and Fathers qualification. Apply the mapping to these columns in the DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "yWoGrjBcca_G", + "metadata": { + "id": "yWoGrjBcca_G" + }, + "outputs": [], + "source": [ + "# Apply the mapping to the appropriate columns in the DataFrame\n", + "data['Previous qualification Bin'] = data['Previous qualification'].map(qualification_bin_mapping)\n", + "data['Mothers qualification Bin'] = data['Mothers qualification'].map(qualification_bin_mapping)\n", + "data['Fathers qualification Bin'] = data['Fathers qualification'].map(qualification_bin_mapping)" + ] + }, + { + "cell_type": "markdown", + "id": "_fv4yzn6cgOt", + "metadata": { + "id": "_fv4yzn6cgOt" + }, + "source": [ + "Confirm below to see the change in the dataframe using .info" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "luo0-rM4chlu", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "luo0-rM4chlu", + "outputId": "ad7fa4ee-203d-4c32-e326-e17109fec126" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 6006 entries, 1 to 2393\n", + "Data columns (total 40 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Marital status 6006 non-null int64 \n", + " 1 Application mode 6006 non-null int64 \n", + " 2 Application order 6006 non-null int64 \n", + " 3 Course 6006 non-null int64 \n", + " 4 Daytime/evening attendance\t 6006 non-null int64 \n", + " 5 Previous qualification 6006 non-null int64 \n", + " 6 Previous qualification (grade) 6006 non-null float64\n", + " 7 Nationality 6006 non-null int64 \n", + " 8 Mothers qualification 6006 non-null int64 \n", + " 9 Fathers qualification 6006 non-null int64 \n", + " 10 Mothers occupation 6006 non-null int64 \n", + " 11 Fathers occupation 6006 non-null int64 \n", + " 12 Admission grade 6006 non-null float64\n", + " 13 Displaced 6006 non-null int64 \n", + " 14 Educational special needs 6006 non-null int64 \n", + " 15 Debtor 6006 non-null int64 \n", + " 16 Tuition fees up to date 6006 non-null int64 \n", + " 17 Gender 6006 non-null int64 \n", + " 18 Scholarship holder 6006 non-null int64 \n", + " 19 Age at enrollment 6006 non-null int64 \n", + " 20 International 6006 non-null int64 \n", + " 21 Curricular units 1st sem (credited) 6006 non-null int64 \n", + " 22 Curricular units 1st sem (enrolled) 6006 non-null int64 \n", + " 23 Curricular units 1st sem (evaluations) 6006 non-null int64 \n", + " 24 Curricular units 1st sem (approved) 6006 non-null int64 \n", + " 25 Curricular units 1st sem (grade) 6006 non-null float64\n", + " 26 Curricular units 1st sem (without evaluations) 6006 non-null int64 \n", + " 27 Curricular units 2nd sem (credited) 6006 non-null int64 \n", + " 28 Curricular units 2nd sem (enrolled) 6006 non-null int64 \n", + " 29 Curricular units 2nd sem (evaluations) 6006 non-null int64 \n", + " 30 Curricular units 2nd sem (approved) 6006 non-null int64 \n", + " 31 Curricular units 2nd sem (grade) 6006 non-null float64\n", + " 32 Curricular units 2nd sem (without evaluations) 6006 non-null int64 \n", + " 33 Unemployment rate 6006 non-null float64\n", + " 34 Inflation rate 6006 non-null float64\n", + " 35 GDP 6006 non-null float64\n", + " 36 TargetEncoded 6006 non-null int64 \n", + " 37 Previous qualification Bin 6006 non-null int64 \n", + " 38 Mothers qualification Bin 6006 non-null int64 \n", + " 39 Fathers qualification Bin 6006 non-null int64 \n", + "dtypes: float64(7), int64(33)\n", + "memory usage: 1.9 MB\n" + ] + } + ], + "source": [ + "data.info()" + ] + }, + { + "cell_type": "markdown", + "id": "wtIwDTJwcs_R", + "metadata": { + "id": "wtIwDTJwcs_R" + }, + "source": [ + "Now remove the 3 original qualtification columns: Previous qualification, Mothers qualification and Fathers qualification." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "Ri-4qgyUct60", + "metadata": { + "id": "Ri-4qgyUct60" + }, + "outputs": [], + "source": [ + "data.drop('Previous qualification', axis=1, inplace=True)\n", + "data.drop('Mothers qualification', axis=1, inplace=True)\n", + "data.drop('Fathers qualification', axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "o8rEvm6zc30e", + "metadata": { + "id": "o8rEvm6zc30e" + }, + "source": [ + "Checking to that the 3 columns are removed by looking at some data." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "qhJ3GYjbc3Qr", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 591 + }, + "id": "qhJ3GYjbc3Qr", + "outputId": "4d13302d-a2da-4a42-d006-373a4df0ab30" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Marital status Application mode Application order Course Daytime/evening attendance\\t Previous qualification (grade) Nationality \\\n", + "1 1 15 1 9254 1 160.0 1 \n", + "3 1 17 2 9773 1 122.0 1 \n", + "4 2 39 1 8014 0 100.0 1 \n", + "5 2 39 1 9991 0 133.1 1 \n", + "6 1 1 1 9500 1 142.0 1 \n", + "8 1 1 3 9238 1 137.0 62 \n", + "10 1 1 1 9670 1 139.0 1 \n", + "11 1 1 1 9500 1 136.0 1 \n", + "13 1 53 1 9254 1 110.0 1 \n", + "14 1 1 1 9085 1 149.0 1 \n", + "16 1 18 1 9238 1 137.0 1 \n", + "17 1 17 2 9500 1 135.0 1 \n", + "18 1 1 1 9130 1 137.0 1 \n", + "19 1 1 1 9853 1 140.0 1 \n", + "20 1 1 3 171 1 122.0 1 \n", + "\n", + " Mothers occupation Fathers occupation Admission grade Displaced Educational special needs Debtor Tuition fees up to date Gender \\\n", + "1 3 3 142.5 1 0 0 0 1 \n", + "3 5 3 119.6 1 0 0 1 0 \n", + "4 9 9 141.5 0 0 0 1 0 \n", + "5 9 7 114.8 0 0 1 1 1 \n", + "6 7 10 128.4 1 0 0 1 0 \n", + "8 9 9 129.3 0 0 0 1 0 \n", + "10 5 7 130.6 1 0 0 1 0 \n", + "11 9 9 119.3 1 0 0 1 0 \n", + "13 4 7 111.8 1 0 0 1 0 \n", + "14 5 5 137.1 1 0 0 1 0 \n", + "16 5 8 137.4 1 0 0 1 0 \n", + "17 5 4 127.3 1 0 0 1 0 \n", + "18 3 5 136.3 1 0 0 1 0 \n", + "19 7 7 124.6 1 0 0 1 0 \n", + "20 9 8 120.3 0 0 0 1 0 \n", + "\n", + " Scholarship holder Age at enrollment International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) \\\n", + "1 0 19 0 0 6 \n", + "3 0 20 0 0 6 \n", + "4 0 45 0 0 6 \n", + "5 0 50 0 0 5 \n", + "6 1 18 0 0 7 \n", + "8 1 21 1 0 6 \n", + "10 0 18 0 0 6 \n", + "11 1 18 0 0 8 \n", + "13 1 21 0 0 6 \n", + "14 1 18 0 0 5 \n", + "16 0 18 0 0 6 \n", + "17 0 18 0 0 7 \n", + "18 0 20 0 0 5 \n", + "19 0 18 0 0 7 \n", + "20 1 21 0 0 0 \n", + "\n", + " Curricular units 1st sem (evaluations) Curricular units 1st sem (approved) Curricular units 1st sem (grade) \\\n", + "1 6 6 14.000000 \n", + "3 8 6 13.428571 \n", + "4 9 5 12.333333 \n", + "5 10 5 11.857143 \n", + "6 9 7 13.300000 \n", + "8 8 6 13.875000 \n", + "10 6 6 12.333333 \n", + "11 8 7 13.214286 \n", + "13 7 6 10.571429 \n", + "14 7 4 13.250000 \n", + "16 10 1 12.000000 \n", + "17 8 7 13.306250 \n", + "18 8 4 12.500000 \n", + "19 7 6 11.666667 \n", + "20 0 0 0.000000 \n", + "\n", + " Curricular units 1st sem (without evaluations) Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) \\\n", + "1 0 0 6 \n", + "3 0 0 6 \n", + "4 0 0 6 \n", + "5 0 0 5 \n", + "6 0 0 8 \n", + "8 0 0 6 \n", + "10 0 0 6 \n", + "11 0 0 8 \n", + "13 0 0 6 \n", + "14 0 0 5 \n", + "16 0 0 6 \n", + "17 0 0 8 \n", + "18 1 0 5 \n", + "19 0 0 7 \n", + "20 0 0 0 \n", + "\n", + " Curricular units 2nd sem (evaluations) Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) \\\n", + "1 6 6 13.666667 \n", + "3 10 5 12.400000 \n", + "4 6 6 13.000000 \n", + "5 17 5 11.500000 \n", + "6 8 8 14.345000 \n", + "8 7 6 14.142857 \n", + "10 7 5 14.200000 \n", + "11 8 7 13.214286 \n", + "13 8 5 11.000000 \n", + "14 5 5 12.000000 \n", + "16 14 2 11.000000 \n", + "17 8 8 14.545000 \n", + "18 8 4 12.250000 \n", + "19 8 6 13.500000 \n", + "20 0 0 0.000000 \n", + "\n", + " Curricular units 2nd sem (without evaluations) Unemployment rate Inflation rate GDP TargetEncoded Previous qualification Bin \\\n", + "1 0 13.9 -0.3 0.79 0 7 \n", + "3 0 9.4 -0.8 -3.12 0 7 \n", + "4 0 13.9 -0.3 0.79 0 7 \n", + "5 5 16.2 0.3 -0.92 0 6 \n", + "6 0 15.5 2.8 -4.06 0 7 \n", + "8 0 16.2 0.3 -0.92 0 7 \n", + "10 0 13.9 -0.3 0.79 0 7 \n", + "11 0 12.7 3.7 -1.70 0 7 \n", + "13 0 8.9 1.4 3.51 0 9 \n", + "14 0 10.8 1.4 1.74 0 7 \n", + "16 0 10.8 1.4 1.74 0 7 \n", + "17 0 15.5 2.8 -4.06 0 7 \n", + "18 2 10.8 1.4 1.74 0 7 \n", + "19 0 16.2 0.3 -0.92 0 7 \n", + "20 0 11.1 0.6 2.02 0 7 \n", + "\n", + " Mothers qualification Bin Fathers qualification Bin \n", + "1 7 9 \n", + "3 5 3 \n", + "4 3 5 \n", + "5 3 3 \n", + "6 6 5 \n", + "8 7 7 \n", + "10 5 6 \n", + "11 6 5 \n", + "13 7 7 \n", + "14 5 3 \n", + "16 6 5 \n", + "17 6 7 \n", + "18 9 6 \n", + "19 6 6 \n", + "20 7 7 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head(15)" + ] + }, + { + "cell_type": "markdown", + "id": "ZY49JetQdISB", + "metadata": { + "id": "ZY49JetQdISB" + }, + "source": [ + "## Marital Status Variable\n", + "Let's look at how the values are distributed in Marital Status." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "dDt3iGHodQyK", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 303 + }, + "id": "dDt3iGHodQyK", + "outputId": "cb905578-7838-4ea0-88b5-7a9ecb56f91c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Marital status\n", + "1 87.16\n", + "2 9.49\n", + "4 2.35\n", + "5 0.72\n", + "6 0.22\n", + "3 0.07\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Marital status'].value_counts(normalize=True, ascending=False).mul(100).round(2)" + ] + }, + { + "cell_type": "markdown", + "id": "4eCd0n1Udas6", + "metadata": { + "id": "4eCd0n1Udas6" + }, + "source": [ + "THe most frequent at 87.16% is 1 - Single, then down to 9.49% for 2 - Married, then 2.35% for 4 - Divorced, and then less than 1% for 5 - Facto Union, 6 - Legally Separated, and 3 - Widower.\n", + "\n", + "Let's bin them to have Single, Married and Divorced on its own bin and the last 3 (Facto Union, Legally Separated, Widower) are bundled together into Other.\n", + "\n", + "Since the current values for Marital Status is from 1-6, I will use 7 to be the Other. I will update the values 5, 6, and 3 to be 7. Refer to the chart below.\n", + "\n", + "\n", + "| Marital Status | Percentage Frequency | New Bin Value | End Result |\n", + "|-------------------------|----------------------|---------------|-------|\n", + "| 1 - single | 87.16|1 | Single |\n", + "| 2 - married | 9.49|2 | Married |\n", + "| 4 - divorced |2.35| 4 | Divorced |\n", + "| 5 - facto union |0.72| 7 | Other |\n", + "| 6 - legally separated |0.22| 7 | Other |\n", + "| 3 - widower | 0.07|7 | Other |\n", + "\n", + "\n", + "We'll create a mapping variable below to imitate the mapping chart above.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "WzyvPD0Xdp9s", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "id": "WzyvPD0Xdp9s", + "outputId": "c81bb319-ea5e-4080-c3ae-c9246ac05a83" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: 1, 2: 2, 3: 7, 4: 4, 5: 7, 6: 7}" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "marital_bin_mapping = {\n", + " 1:1,\n", + " 2:2,\n", + " 3:7,\n", + " 4:4,\n", + " 5:7,\n", + " 6:7\n", + "}\n", + "\n", + "display(marital_bin_mapping)" + ] + }, + { + "cell_type": "markdown", + "id": "SHoBTYOCdyeC", + "metadata": { + "id": "SHoBTYOCdyeC" + }, + "source": [ + "Using the variable above we can go ahead and map the Marital status and create a new one called Marital Status Bin." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "M-MBq5Szdz_Q", + "metadata": { + "id": "M-MBq5Szdz_Q" + }, + "outputs": [], + "source": [ + "# Apply the mapping to the appropriate column\n", + "data['Marital Status Bin'] = data['Marital status'].map(marital_bin_mapping)" + ] + }, + { + "cell_type": "markdown", + "id": "hjvC4db_d5ig", + "metadata": { + "id": "hjvC4db_d5ig" + }, + "source": [ + "Confirm the changes. Check the new columns values and distribution." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "9pU8eGmId8Rg", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 382 + }, + "id": "9pU8eGmId8Rg", + "outputId": "d65b98e0-d87f-41de-d434-4973cda2b843" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Marital Status Bin\n", + "1 87.16\n", + "2 9.49\n", + "4 2.35\n", + "7 1.00\n", + "Name: proportion, dtype: float64\n" + ] + }, + { + "data": { + "text/html": [ + "
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Marital statusApplication modeApplication orderCourseDaytime/evening attendance\\tPrevious qualification (grade)NationalityMothers occupationFathers occupationAdmission gradeDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentInternationalCurricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Unemployment rateInflation rateGDPTargetEncodedPrevious qualification BinMothers qualification BinFathers qualification BinMarital Status Bin
1115192541160.0133142.5100010190066614.0000000066613.666667013.9-0.30.7907791
3117297731122.0153119.6100100200068613.42857100610512.40000009.4-0.8-3.1207531
4239180140100.0199141.5000100450069512.3333330066613.000000013.9-0.30.7907352
5239199910133.1197114.80011105000510511.85714300517511.500000516.20.3-0.9206332
611195001142.01710128.4100101180079713.3000000088814.345000015.52.8-4.0607651
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" + ], + "text/plain": [ + " Marital status Application mode Application order Course Daytime/evening attendance\\t Previous qualification (grade) Nationality \\\n", + "1 1 15 1 9254 1 160.0 1 \n", + "3 1 17 2 9773 1 122.0 1 \n", + "4 2 39 1 8014 0 100.0 1 \n", + "5 2 39 1 9991 0 133.1 1 \n", + "6 1 1 1 9500 1 142.0 1 \n", + "\n", + " Mothers occupation Fathers occupation Admission grade Displaced Educational special needs Debtor Tuition fees up to date Gender \\\n", + "1 3 3 142.5 1 0 0 0 1 \n", + "3 5 3 119.6 1 0 0 1 0 \n", + "4 9 9 141.5 0 0 0 1 0 \n", + "5 9 7 114.8 0 0 1 1 1 \n", + "6 7 10 128.4 1 0 0 1 0 \n", + "\n", + " Scholarship holder Age at enrollment International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) \\\n", + "1 0 19 0 0 6 \n", + "3 0 20 0 0 6 \n", + "4 0 45 0 0 6 \n", + "5 0 50 0 0 5 \n", + "6 1 18 0 0 7 \n", + "\n", + " Curricular units 1st sem (evaluations) Curricular units 1st sem (approved) Curricular units 1st sem (grade) \\\n", + "1 6 6 14.000000 \n", + "3 8 6 13.428571 \n", + "4 9 5 12.333333 \n", + "5 10 5 11.857143 \n", + "6 9 7 13.300000 \n", + "\n", + " Curricular units 1st sem (without evaluations) Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) \\\n", + "1 0 0 6 \n", + "3 0 0 6 \n", + "4 0 0 6 \n", + "5 0 0 5 \n", + "6 0 0 8 \n", + "\n", + " Curricular units 2nd sem (evaluations) Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) \\\n", + "1 6 6 13.666667 \n", + "3 10 5 12.400000 \n", + "4 6 6 13.000000 \n", + "5 17 5 11.500000 \n", + "6 8 8 14.345000 \n", + "\n", + " Curricular units 2nd sem (without evaluations) Unemployment rate Inflation rate GDP TargetEncoded Previous qualification Bin \\\n", + "1 0 13.9 -0.3 0.79 0 7 \n", + "3 0 9.4 -0.8 -3.12 0 7 \n", + "4 0 13.9 -0.3 0.79 0 7 \n", + "5 5 16.2 0.3 -0.92 0 6 \n", + "6 0 15.5 2.8 -4.06 0 7 \n", + "\n", + " Mothers qualification Bin Fathers qualification Bin Marital Status Bin \n", + "1 7 9 1 \n", + "3 5 3 1 \n", + "4 3 5 2 \n", + "5 3 3 2 \n", + "6 6 5 1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(data['Marital Status Bin'].value_counts(normalize=True, ascending=False).mul(100).round(2))\n", + "display(data.head())" + ] + }, + { + "cell_type": "markdown", + "id": "cIyMMdhneFnH", + "metadata": { + "id": "cIyMMdhneFnH" + }, + "source": [ + "We see that the values for Facto Union, Legally Separated, Widower are now all under 7 in the new Marital Status Bin column. Let's go ahead and delete the original Marital status column and view the dataframe to confirm." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "qLaMkf9meGzP", + "metadata": { + "id": "qLaMkf9meGzP" + }, + "outputs": [], + "source": [ + "data.drop('Marital status', axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "Lkae_pffeLzx", + "metadata": { + "id": "Lkae_pffeLzx" + }, + "source": [ + "Have a look at the data to check once again." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "Hb2eNPUleNKY", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 591 + }, + "id": "Hb2eNPUleNKY", + "outputId": "cb92930b-f684-4c8a-d6f5-9ec6b8155ae2" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Application modeApplication orderCourseDaytime/evening attendance\\tPrevious qualification (grade)NationalityMothers occupationFathers occupationAdmission gradeDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentInternationalCurricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Unemployment rateInflation rateGDPTargetEncodedPrevious qualification BinMothers qualification BinFathers qualification BinMarital Status Bin
115192541160.0133142.5100010190066614.0000000066613.666667013.9-0.30.7907791
317297731122.0153119.6100100200068613.42857100610512.40000009.4-0.8-3.1207531
439180140100.0199141.5000100450069512.3333330066613.000000013.9-0.30.7907352
539199910133.1197114.80011105000510511.85714300517511.500000516.20.3-0.9206332
61195001142.01710128.4100101180079713.3000000088814.345000015.52.8-4.0607651
81392381137.06299129.3000101211068613.8750000067614.142857016.20.3-0.9207771
101196701139.0157130.6100100180066612.3333330067514.200000013.9-0.30.7907561
111195001136.0199119.3100101180088713.2142860088713.214286012.73.7-1.7007651
1353192541110.0147111.8100101210067610.5714290068511.00000008.91.43.5109771
141190851149.0155137.1100101180057413.2500000055512.000000010.81.41.7407531
1618192381137.0158137.41001001800610112.00000000614211.000000010.81.41.7407651
1717295001135.0154127.3100100180078713.3062500088814.545000015.52.8-4.0607671
181191301137.0135136.3100100200058412.5000001058412.250000210.81.41.7407961
191198531140.0177124.6100100180077611.6666670078613.500000016.20.3-0.9207661
20131711122.0198120.300010121000000.000000000000.000000011.10.62.0207771
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" + ], + "text/plain": [ + " Application mode Application order Course Daytime/evening attendance\\t Previous qualification (grade) Nationality Mothers occupation \\\n", + "1 15 1 9254 1 160.0 1 3 \n", + "3 17 2 9773 1 122.0 1 5 \n", + "4 39 1 8014 0 100.0 1 9 \n", + "5 39 1 9991 0 133.1 1 9 \n", + "6 1 1 9500 1 142.0 1 7 \n", + "8 1 3 9238 1 137.0 62 9 \n", + "10 1 1 9670 1 139.0 1 5 \n", + "11 1 1 9500 1 136.0 1 9 \n", + "13 53 1 9254 1 110.0 1 4 \n", + "14 1 1 9085 1 149.0 1 5 \n", + "16 18 1 9238 1 137.0 1 5 \n", + "17 17 2 9500 1 135.0 1 5 \n", + "18 1 1 9130 1 137.0 1 3 \n", + "19 1 1 9853 1 140.0 1 7 \n", + "20 1 3 171 1 122.0 1 9 \n", + "\n", + " Fathers occupation Admission grade Displaced Educational special needs Debtor Tuition fees up to date Gender Scholarship holder \\\n", + "1 3 142.5 1 0 0 0 1 0 \n", + "3 3 119.6 1 0 0 1 0 0 \n", + "4 9 141.5 0 0 0 1 0 0 \n", + "5 7 114.8 0 0 1 1 1 0 \n", + "6 10 128.4 1 0 0 1 0 1 \n", + "8 9 129.3 0 0 0 1 0 1 \n", + "10 7 130.6 1 0 0 1 0 0 \n", + "11 9 119.3 1 0 0 1 0 1 \n", + "13 7 111.8 1 0 0 1 0 1 \n", + "14 5 137.1 1 0 0 1 0 1 \n", + "16 8 137.4 1 0 0 1 0 0 \n", + "17 4 127.3 1 0 0 1 0 0 \n", + "18 5 136.3 1 0 0 1 0 0 \n", + "19 7 124.6 1 0 0 1 0 0 \n", + "20 8 120.3 0 0 0 1 0 1 \n", + "\n", + " Age at enrollment International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) \\\n", + "1 19 0 0 6 \n", + "3 20 0 0 6 \n", + "4 45 0 0 6 \n", + "5 50 0 0 5 \n", + "6 18 0 0 7 \n", + "8 21 1 0 6 \n", + "10 18 0 0 6 \n", + "11 18 0 0 8 \n", + "13 21 0 0 6 \n", + "14 18 0 0 5 \n", + "16 18 0 0 6 \n", + "17 18 0 0 7 \n", + "18 20 0 0 5 \n", + "19 18 0 0 7 \n", + "20 21 0 0 0 \n", + "\n", + " Curricular units 1st sem (evaluations) Curricular units 1st sem (approved) Curricular units 1st sem (grade) \\\n", + "1 6 6 14.000000 \n", + "3 8 6 13.428571 \n", + "4 9 5 12.333333 \n", + "5 10 5 11.857143 \n", + "6 9 7 13.300000 \n", + "8 8 6 13.875000 \n", + "10 6 6 12.333333 \n", + "11 8 7 13.214286 \n", + "13 7 6 10.571429 \n", + "14 7 4 13.250000 \n", + "16 10 1 12.000000 \n", + "17 8 7 13.306250 \n", + "18 8 4 12.500000 \n", + "19 7 6 11.666667 \n", + "20 0 0 0.000000 \n", + "\n", + " Curricular units 1st sem (without evaluations) Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) \\\n", + "1 0 0 6 \n", + "3 0 0 6 \n", + "4 0 0 6 \n", + "5 0 0 5 \n", + "6 0 0 8 \n", + "8 0 0 6 \n", + "10 0 0 6 \n", + "11 0 0 8 \n", + "13 0 0 6 \n", + "14 0 0 5 \n", + "16 0 0 6 \n", + "17 0 0 8 \n", + "18 1 0 5 \n", + "19 0 0 7 \n", + "20 0 0 0 \n", + "\n", + " Curricular units 2nd sem (evaluations) Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) \\\n", + "1 6 6 13.666667 \n", + "3 10 5 12.400000 \n", + "4 6 6 13.000000 \n", + "5 17 5 11.500000 \n", + "6 8 8 14.345000 \n", + "8 7 6 14.142857 \n", + "10 7 5 14.200000 \n", + "11 8 7 13.214286 \n", + "13 8 5 11.000000 \n", + "14 5 5 12.000000 \n", + "16 14 2 11.000000 \n", + "17 8 8 14.545000 \n", + "18 8 4 12.250000 \n", + "19 8 6 13.500000 \n", + "20 0 0 0.000000 \n", + "\n", + " Curricular units 2nd sem (without evaluations) Unemployment rate Inflation rate GDP TargetEncoded Previous qualification Bin \\\n", + "1 0 13.9 -0.3 0.79 0 7 \n", + "3 0 9.4 -0.8 -3.12 0 7 \n", + "4 0 13.9 -0.3 0.79 0 7 \n", + "5 5 16.2 0.3 -0.92 0 6 \n", + "6 0 15.5 2.8 -4.06 0 7 \n", + "8 0 16.2 0.3 -0.92 0 7 \n", + "10 0 13.9 -0.3 0.79 0 7 \n", + "11 0 12.7 3.7 -1.70 0 7 \n", + "13 0 8.9 1.4 3.51 0 9 \n", + "14 0 10.8 1.4 1.74 0 7 \n", + "16 0 10.8 1.4 1.74 0 7 \n", + "17 0 15.5 2.8 -4.06 0 7 \n", + "18 2 10.8 1.4 1.74 0 7 \n", + "19 0 16.2 0.3 -0.92 0 7 \n", + "20 0 11.1 0.6 2.02 0 7 \n", + "\n", + " Mothers qualification Bin Fathers qualification Bin Marital Status Bin \n", + "1 7 9 1 \n", + "3 5 3 1 \n", + "4 3 5 2 \n", + "5 3 3 2 \n", + "6 6 5 1 \n", + "8 7 7 1 \n", + "10 5 6 1 \n", + "11 6 5 1 \n", + "13 7 7 1 \n", + "14 5 3 1 \n", + "16 6 5 1 \n", + "17 6 7 1 \n", + "18 9 6 1 \n", + "19 6 6 1 \n", + "20 7 7 1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(data.head(15))" + ] + }, + { + "cell_type": "markdown", + "id": "jTIIVEROedkB", + "metadata": { + "id": "jTIIVEROedkB" + }, + "source": [ + "Great. No more *Marital status* column. It has been replaced with *Marital Status Bin* that has only 4 category values.\n", + "\n", + "Let's look at what else is left. Below is the same list I created earlier above after looking through the data and seeing the high cardinality categorical columns that need decreasing.\n", + "\n", + "- Application mode\n", + "- Application order\n", + "- Course\n", + "- Fathers occupation\n", + "- Mothers occupation\n", + "- Nationality\n", + "- ~~Marital status~~\n", + "- ~~Previous qualification~~\n", + "- ~~Fathers qualification~~\n", + "- ~~Mothers qualification~~\n", + "\n", + "\n", + "Onto the *Nationality* column now." + ] + }, + { + "cell_type": "markdown", + "id": "A6s7VHnZej1f", + "metadata": { + "id": "A6s7VHnZej1f" + }, + "source": [ + "## Binning Nationality Variable\n", + "\n", + "Look again at the distribution of the *Nationality* column using .value_counts descending from most to least frequency.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "xp1Z_KQEeqqj", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xp1Z_KQEeqqj", + "outputId": "2dce53db-46de-43b9-d5f5-738387459da9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nationality\n", + "1 97.60\n", + "41 0.88\n", + "22 0.35\n", + "6 0.28\n", + "26 0.25\n", + "21 0.10\n", + "103 0.08\n", + "24 0.07\n", + "11 0.05\n", + "17 0.05\n", + "101 0.05\n", + "62 0.03\n", + "25 0.03\n", + "109 0.03\n", + "2 0.03\n", + "32 0.02\n", + "13 0.02\n", + "108 0.02\n", + "100 0.02\n", + "14 0.02\n", + "105 0.02\n", + "Name: proportion, dtype: float64\n", + "Total rows: (21,)\n" + ] + } + ], + "source": [ + "nationality_breakdown = data['Nationality'].value_counts(normalize=True, ascending=False).mul(100).round(2)\n", + "print(nationality_breakdown)\n", + "print('Total rows:', nationality_breakdown.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "YILxKd-Ce6Wu", + "metadata": { + "id": "YILxKd-Ce6Wu" + }, + "source": [ + "So 97.5% of the data is *1 - Portuguese* while the rest of the other 20 items tally up to only 2.5%. I think it safe to bin the 20 items to *Other*. Let's work on that.\n", + "\n", + "We will map the following:\n", + "\n", + "| Nationality | Percentage Frequency | New Bin Value | End Result |\n", + "|--------------------------------|----------------------|-----------|------------|\n", + "| 1 - Portuguese | 97.60 | 1 | Portuguese |\n", + "| 41 - Brazilian | 0.88 | 0 | Other |\n", + "| 22 - Cape Verdean | 0.35 | 0 | Other |\n", + "| 6 - Spanish | 0.38 | 0 | Other |\n", + "| 26 - Santomean | 0.25 | 0 | Other |\n", + "| 21 - Angolan | 0.10 | 0 | Other |\n", + "| 103 - Ukrainian | 0.08 | 0 | Other |\n", + "| 24 - Guinean | 0.07 | 0 | Other |\n", + "| 101 - Mexican | 0.05 | 0 | Other |\n", + "| 11 - Italian | 0.05 | 0 | Other |\n", + "| 17 - Lithuanian | 0.05 | 0 | Other |\n", + "| 62 - Romanian | 0.03 | 0 | Other |\n", + "| 25 - Mozambican | 0.03 | 0 | Other |\n", + "| 2 - German | 0.03 | 0 | Other |\n", + "| 109 - Colombian | 0.03 | 0 | Other |\n", + "| 32 - Turkish | 0.02 | 0 | Other |\n", + "| 13 - Dutch | 0.02 | 0 | Other |\n", + "| 100 - Moldova (Republic of) | 0.02 | 0 | Other |\n", + "| 108 - Cuban | 0.02 | 0 | Other |\n", + "| 105 - Russian | 0.02 | 0 | Other |\n", + "| 14 - English | 0.02 | 0 | Other |\n", + "\n", + "\n", + "This is simple enough so we will not need to use .map. We'll just create a new column based on the condition, if *Nationality* is 1 set new *Nationality Bin* column to 1 and all other values in *Nationality* set *Nationality Bin* to 0." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "IiPANrs_fBVT", + "metadata": { + "id": "IiPANrs_fBVT" + }, + "outputs": [], + "source": [ + "data['Nationality Bin'] = np.where(data['Nationality'] == 1, 1, 0)" + ] + }, + { + "cell_type": "markdown", + "id": "MK7jWPHyfHCr", + "metadata": { + "id": "MK7jWPHyfHCr" + }, + "source": [ + "Confirm the new column with sample. Then look at the new column next to the original to compare" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "-TNegt0FfIK1", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 623 + }, + "id": "-TNegt0FfIK1", + "outputId": "b11aa6af-23cd-4994-da4f-8ba5b333a4ae" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Nationality Nationality Bin\n", + "1450 1 1\n", + "2527 1 1\n", + "54 1 1\n", + "3462 1 1\n", + "3311 1 1\n", + "4157 1 1\n", + "1909 1 1\n", + "1318 1 1\n", + "2888 1 1\n", + "636 1 1" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(data.sample(5))\n", + "display(data[['Nationality', 'Nationality Bin']].sample(10))" + ] + }, + { + "cell_type": "markdown", + "id": "oi17AiD5fR7f", + "metadata": { + "id": "oi17AiD5fR7f" + }, + "source": [ + "Grab the value count on the new Nationality Bin column." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "RskwNH4GfU-r", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RskwNH4GfU-r", + "outputId": "17dd65e0-5043-4780-ea59-c497e03d7fe9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nationality Bin\n", + "1 97.6\n", + "0 2.4\n", + "Name: proportion, dtype: float64\n" + ] + } + ], + "source": [ + "print(data['Nationality Bin'].value_counts(normalize=True, ascending=False).mul(100).round(2))" + ] + }, + { + "cell_type": "markdown", + "id": "MDT_OXbyfa91", + "metadata": { + "id": "MDT_OXbyfa91" + }, + "source": [ + "Looks good. 97.6% are 1 which is Portugese and the rest of the 2.4% are Others. Now let's remove the original column." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "NNfwrqc0fcBM", + "metadata": { + "id": "NNfwrqc0fcBM" + }, + "outputs": [], + "source": [ + "data.drop('Nationality', axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "eeZ_aJZ5fgZv", + "metadata": { + "id": "eeZ_aJZ5fgZv" + }, + "source": [ + "Check the data again to confirm." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "fmGuVCX4fjH7", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 435 + }, + "id": "fmGuVCX4fjH7", + "outputId": "8ee57aa1-34a2-4b14-82ee-cad8ec889562" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Application mode Application order Course Daytime/evening attendance\\t Previous qualification (grade) Mothers occupation \\\n", + "1 15 1 9254 1 160.0 3 \n", + "3 17 2 9773 1 122.0 5 \n", + "4 39 1 8014 0 100.0 9 \n", + "5 39 1 9991 0 133.1 9 \n", + "6 1 1 9500 1 142.0 7 \n", + "8 1 3 9238 1 137.0 9 \n", + "10 1 1 9670 1 139.0 5 \n", + "11 1 1 9500 1 136.0 9 \n", + "13 53 1 9254 1 110.0 4 \n", + "14 1 1 9085 1 149.0 5 \n", + "\n", + " Fathers occupation Admission grade Displaced Educational special needs Debtor Tuition fees up to date Gender Scholarship holder \\\n", + "1 3 142.5 1 0 0 0 1 0 \n", + "3 3 119.6 1 0 0 1 0 0 \n", + "4 9 141.5 0 0 0 1 0 0 \n", + "5 7 114.8 0 0 1 1 1 0 \n", + "6 10 128.4 1 0 0 1 0 1 \n", + "8 9 129.3 0 0 0 1 0 1 \n", + "10 7 130.6 1 0 0 1 0 0 \n", + "11 9 119.3 1 0 0 1 0 1 \n", + "13 7 111.8 1 0 0 1 0 1 \n", + "14 5 137.1 1 0 0 1 0 1 \n", + "\n", + " Age at enrollment International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) \\\n", + "1 19 0 0 6 \n", + "3 20 0 0 6 \n", + "4 45 0 0 6 \n", + "5 50 0 0 5 \n", + "6 18 0 0 7 \n", + "8 21 1 0 6 \n", + "10 18 0 0 6 \n", + "11 18 0 0 8 \n", + "13 21 0 0 6 \n", + "14 18 0 0 5 \n", + "\n", + " Curricular units 1st sem (evaluations) Curricular units 1st sem (approved) Curricular units 1st sem (grade) \\\n", + "1 6 6 14.000000 \n", + "3 8 6 13.428571 \n", + "4 9 5 12.333333 \n", + "5 10 5 11.857143 \n", + "6 9 7 13.300000 \n", + "8 8 6 13.875000 \n", + "10 6 6 12.333333 \n", + "11 8 7 13.214286 \n", + "13 7 6 10.571429 \n", + "14 7 4 13.250000 \n", + "\n", + " Curricular units 1st sem (without evaluations) Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) \\\n", + "1 0 0 6 \n", + "3 0 0 6 \n", + "4 0 0 6 \n", + "5 0 0 5 \n", + "6 0 0 8 \n", + "8 0 0 6 \n", + "10 0 0 6 \n", + "11 0 0 8 \n", + "13 0 0 6 \n", + "14 0 0 5 \n", + "\n", + " Curricular units 2nd sem (evaluations) Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) \\\n", + "1 6 6 13.666667 \n", + "3 10 5 12.400000 \n", + "4 6 6 13.000000 \n", + "5 17 5 11.500000 \n", + "6 8 8 14.345000 \n", + "8 7 6 14.142857 \n", + "10 7 5 14.200000 \n", + "11 8 7 13.214286 \n", + "13 8 5 11.000000 \n", + "14 5 5 12.000000 \n", + "\n", + " Curricular units 2nd sem (without evaluations) Unemployment rate Inflation rate GDP TargetEncoded Previous qualification Bin \\\n", + "1 0 13.9 -0.3 0.79 0 7 \n", + "3 0 9.4 -0.8 -3.12 0 7 \n", + "4 0 13.9 -0.3 0.79 0 7 \n", + "5 5 16.2 0.3 -0.92 0 6 \n", + "6 0 15.5 2.8 -4.06 0 7 \n", + "8 0 16.2 0.3 -0.92 0 7 \n", + "10 0 13.9 -0.3 0.79 0 7 \n", + "11 0 12.7 3.7 -1.70 0 7 \n", + "13 0 8.9 1.4 3.51 0 9 \n", + "14 0 10.8 1.4 1.74 0 7 \n", + "\n", + " Mothers qualification Bin Fathers qualification Bin Marital Status Bin Nationality Bin \n", + "1 7 9 1 1 \n", + "3 5 3 1 1 \n", + "4 3 5 2 1 \n", + "5 3 3 2 1 \n", + "6 6 5 1 1 \n", + "8 7 7 1 0 \n", + "10 5 6 1 1 \n", + "11 6 5 1 1 \n", + "13 7 7 1 1 \n", + "14 5 3 1 1 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(data.head(10))" + ] + }, + { + "cell_type": "markdown", + "id": "wrt6ffTZfyXE", + "metadata": { + "id": "wrt6ffTZfyXE" + }, + "source": [ + "Let's cross off Nationality and move onto the next.\n", + "\n", + "- Application mode\n", + "- Application order\n", + "- Course\n", + "- Fathers occupation\n", + "- Mothers occupation\n", + "- ~~Nationality~~\n", + "- ~~Marital status~~\n", + "- ~~Previous qualification~~\n", + "- ~~Fathers qualification~~\n", + "- ~~Mothers qualification~~\n", + "\n", + "Onto the *Occupation* columns now." + ] + }, + { + "cell_type": "markdown", + "id": "Qun0O7qgf7ms", + "metadata": { + "id": "Qun0O7qgf7ms" + }, + "source": [ + "Occupation Variables\n", + "#### Binning Occupation Variables\n", + "\n", + "##### Father's Occupation\n", + "\n", + "\n", + "Look again at the distribution of the *Fathers occupation* column using .value_counts descending from most to least frequency and the count of the distinct values.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "q5gFBBlmgFbB", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "q5gFBBlmgFbB", + "outputId": "8ceb57b0-c9c3-48ae-8e1d-6e65e303d2a9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total values: (46,)\n", + "Fathers occupation\n", + "9 22.64\n", + "7 15.13\n", + "5 11.24\n", + "3 8.79\n", + "4 8.39\n", + "8 6.96\n", + "10 5.79\n", + "6 5.53\n", + "2 4.81\n", + "0 3.68\n", + "1 2.96\n", + "90 1.70\n", + "99 0.62\n", + "193 0.30\n", + "144 0.13\n", + "171 0.13\n", + "152 0.12\n", + "192 0.10\n", + "103 0.08\n", + "163 0.08\n", + "194 0.07\n", + "175 0.07\n", + "112 0.07\n", + "181 0.05\n", + "183 0.05\n", + "123 0.05\n", + "135 0.05\n", + "102 0.03\n", + "182 0.03\n", + "151 0.03\n", + "172 0.03\n", + "122 0.03\n", + "124 0.02\n", + "131 0.02\n", + "195 0.02\n", + "121 0.02\n", + "132 0.02\n", + "161 0.02\n", + "143 0.02\n", + "134 0.02\n", + "153 0.02\n", + "174 0.02\n", + "141 0.02\n", + "114 0.02\n", + "101 0.02\n", + "154 0.02\n", + "Name: proportion, dtype: float64\n" + ] + } + ], + "source": [ + "father_occupation_frequency_percentages = data['Fathers occupation'].value_counts(normalize=True, ascending=False).mul(100).round(2)\n", + "print('Total values:', father_occupation_frequency_percentages.shape)\n", + "print(father_occupation_frequency_percentages)" + ] + }, + { + "cell_type": "markdown", + "id": "vdMayjb0gOC0", + "metadata": { + "id": "vdMayjb0gOC0" + }, + "source": [ + "46 categories for Fathers occupation and it looks like the majority of them are less than 1% in frequency. How many are more than 1%? Filter to show only those values with more than 1% frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "pArrXZkdgR5P", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pArrXZkdgR5P", + "outputId": "29d7783a-7ff9-4b72-d7d0-8837e4c966f4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fathers occupation\n", + "9 22.64\n", + "7 15.13\n", + "5 11.24\n", + "3 8.79\n", + "4 8.39\n", + "8 6.96\n", + "10 5.79\n", + "6 5.53\n", + "2 4.81\n", + "0 3.68\n", + "1 2.96\n", + "90 1.70\n", + "Name: proportion, dtype: float64\n", + "Total categories over 1 percent frequeancy: (12,)\n" + ] + } + ], + "source": [ + "print(father_occupation_frequency_percentages[father_occupation_frequency_percentages > 1])\n", + "print('Total categories over 1 percent frequeancy:', father_occupation_frequency_percentages[father_occupation_frequency_percentages > 1].shape)" + ] + }, + { + "cell_type": "markdown", + "id": "j9A2shw6gg9M", + "metadata": { + "id": "j9A2shw6gg9M" + }, + "source": [ + "Out of the 46 categories, 12 of them are over 1% frequency. We can bin down to 13 categories, keeping the 12 that are over 1% and the rest will be bundled into *Other*, just like the pervious categorical variables have been binned.\n", + "\n", + "| Fathers Occupation | Percentage Frequency | New Bin Value | Description |\n", + "|---------------------------------------------------------------------------------------------------------------------------|----------------------|---------------|---------------------------------------------------------------------------------------------------|\n", + "| 9 - Unskilled Workers | 22.64% | 9 | Unskilled Workers |\n", + "| 7 - Skilled Workers in Industry, Construction and Craftsmen | 15.13% | 7 | Skilled Workers in Industry, Construction and Craftsmen |\n", + "| 5 - Personal Services, Security and Safety Workers and Sellers | 11.24% | 5 | Personal Services, Security and Safety Workers and Sellers |\n", + "| 3 - Intermediate Level Technicians and Professions | 8.79% | 3 | Intermediate Level Technicians and Professions |\n", + "| 4 - Administrative staff | 8.39% | 4 | Administrative staff |\n", + "| 8 - Installation and Machine Operators and Assembly Workers | 6.96% | 8 | Installation and Machine Operators and Assembly Workers |\n", + "| 10 - Armed Forces Professions | 5.79% | 10 | Armed Forces Professions |\n", + "| 6 - Farmers and Skilled Workers in Agriculture, Fisheries and Forestry | 5.53% | 6 | Farmers and Skilled Workers in Agriculture, Fisheries and Forestry |\n", + "| 2 - Specialists in Intellectual and Scientific Activities | 4.81% | 2 | Specialists in Intellectual and Scientific Activities |\n", + "| 0 - Student | 3.68% | 0 | Student |\n", + "| 1 - Representatives of the Legislative Power and Executive Bodies, Directors, Directors and Executive Managers | 2.96% | 1 | Representatives of the Legislative Power and Executive Bodies, Directors, Directors and Executive Managers |\n", + "| 90 - Other Situation | 1.7% | 90 | Other Situation |\n", + " All other values not in the list above | | 200 | Other (New Bundle)|\n", + "\n", + "\n", + "\n", + "\n", + "All the other categories not on this list will be set to a new value called *Other* which will be different from the value *90 - Other Situation*. We'll use an integer higher than the maximum value of this variable. Let's find that max value.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "HRLiJNPigmhr", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HRLiJNPigmhr", + "outputId": "9aa8fbe8-01ea-4f64-9453-5050322aa8ad" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "195" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Fathers occupation'].max()" + ] + }, + { + "cell_type": "markdown", + "id": "_kTTJSF1guXf", + "metadata": { + "id": "_kTTJSF1guXf" + }, + "source": [ + "The max value for Father's occupation is 195. Since we have to do the same for Mother's occupation let use the same one, making sure that it's also pass the maximum value of the Mother's occupation column too." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "t2QqX5n8gwjv", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "t2QqX5n8gwjv", + "outputId": "725bd65e-f4a2-4fc8-b68d-e220fe570aad" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "194" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Mothers occupation'].max()" + ] + }, + { + "cell_type": "markdown", + "id": "eGxSmwWog2io", + "metadata": { + "id": "eGxSmwWog2io" + }, + "source": [ + "Father's occupation is 195 while Mother's occupation is 194. Let's use 200 as the new value for the bundled Other. We'll map the values above 1% frequency according to the chart above and the rest will be set to 200. Let's create the mapping variable. Find the occupation ID values that are over 1% frequency in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "bISepWUtg50E", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bISepWUtg50E", + "outputId": "e3eaa4e8-3f69-41aa-b52a-3fbfd4dba5a8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[9, 7, 5, 3, 4, 8, 10, 6, 2, 0, 1, 90]\n" + ] + } + ], + "source": [ + "print(father_occupation_frequency_percentages[father_occupation_frequency_percentages > 1].index.tolist())" + ] + }, + { + "cell_type": "markdown", + "id": "-Algm1Veg-cz", + "metadata": { + "id": "-Algm1Veg-cz" + }, + "source": [ + "Grab the rows in the dataframe that have these occupation value IDs that are over 1% frequency and store it in a variable called father_occupation_condition." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "8GZQy6zShAxi", + "metadata": { + "id": "8GZQy6zShAxi" + }, + "outputs": [], + "source": [ + "father_occupation_condition = data['Fathers occupation'].isin([9, 7, 5, 3, 4, 8, 10, 6, 2, 0, 1, 90])" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "Sfmp62c3hBwD", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 397 + }, + "id": "Sfmp62c3hBwD", + "outputId": "250ee521-80b2-4f58-b3fb-9d005777034f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1639 True\n", + "2774 True\n", + "2629 True\n", + "2393 True\n", + "566 True\n", + "46 True\n", + "620 True\n", + "4021 True\n", + "3001 True\n", + "2389 True\n", + "Name: Fathers occupation, dtype: bool" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "father_occupation_condition.sample(10)" + ] + }, + { + "cell_type": "markdown", + "id": "DsZE0UElhLXA", + "metadata": { + "id": "DsZE0UElhLXA" + }, + "source": [ + "Looks like the .isin gives a true value if the row has these set of values. How many are there?" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "6gmpDvDwhOSZ", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6gmpDvDwhOSZ", + "outputId": "355ada12-4aa5-4b99-a90b-e09db1e22870" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fathers occupation\n", + "True 5864\n", + "False 142\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "print(father_occupation_condition.value_counts())" + ] + }, + { + "cell_type": "markdown", + "id": "1sThIhElhVwJ", + "metadata": { + "id": "1sThIhElhVwJ" + }, + "source": [ + "There are 5864 records out of the 6006 entire data. Create a new column called Fathers occupation Bin and keep the original occupation value IDs of the ones that are over 1% frequency and set the others (142 of them) to 200 which we defined to indicate as other Other." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "RVGmtJCIhbF3", + "metadata": { + "id": "RVGmtJCIhbF3" + }, + "outputs": [], + "source": [ + "data['Fathers occupation Bin'] = np.where(father_occupation_condition, data['Fathers occupation'], 200) # Setting other values to 200" + ] + }, + { + "cell_type": "markdown", + "id": "MuMBwUE-hohR", + "metadata": { + "id": "MuMBwUE-hohR" + }, + "source": [ + "Have a look to confirm." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "DQlz-0wIhgKh", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 278 + }, + "id": "DQlz-0wIhgKh", + "outputId": "b15a9c60-f1d3-475a-b422-a475f00cc689" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Application modeApplication orderCourseDaytime/evening attendance\\tPrevious qualification (grade)Mothers occupationFathers occupationAdmission gradeDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentInternationalCurricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Unemployment rateInflation rateGDPTargetEncodedPrevious qualification BinMothers qualification BinFathers qualification BinMarital Status BinNationality BinFathers occupation Bin
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" + ], + "text/plain": [ + " Application mode Application order Course Daytime/evening attendance\\t Previous qualification (grade) Mothers occupation \\\n", + "2524 17 4 9238 1 133.1 9 \n", + "3199 1 1 9500 1 148.0 9 \n", + "4372 39 1 9003 1 130.0 9 \n", + "4288 44 1 9003 1 130.0 9 \n", + "935 1 3 171 1 145.0 3 \n", + "\n", + " Fathers occupation Admission grade Displaced Educational special needs Debtor Tuition fees up to date Gender Scholarship holder \\\n", + "2524 5 95.0 1 0 0 1 0 1 \n", + "3199 9 127.0 1 0 0 1 1 0 \n", + "4372 9 126.1 0 0 0 1 1 0 \n", + "4288 2 130.0 1 0 1 0 1 0 \n", + "935 3 140.1 1 0 0 1 0 0 \n", + "\n", + " Age at enrollment International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) \\\n", + "2524 19 0 0 6 \n", + "3199 20 0 0 8 \n", + "4372 24 0 0 6 \n", + "4288 23 0 0 6 \n", + "935 20 0 6 12 \n", + "\n", + " Curricular units 1st sem (evaluations) Curricular units 1st sem (approved) Curricular units 1st sem (grade) \\\n", + "2524 12 6 11.666667 \n", + "3199 8 7 12.841429 \n", + "4372 7 0 0.000000 \n", + "4288 12 2 11.333333 \n", + "935 12 12 13.250000 \n", + "\n", + " Curricular units 1st sem (without evaluations) Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) \\\n", + "2524 0 0 6 \n", + "3199 0 0 8 \n", + "4372 0 0 6 \n", + "4288 0 0 6 \n", + "935 0 4 11 \n", + "\n", + " Curricular units 2nd sem (evaluations) Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) \\\n", + "2524 7 0 0.000000 \n", + "3199 8 7 12.841429 \n", + "4372 10 0 0.000000 \n", + "4288 8 2 13.000000 \n", + "935 14 11 14.333333 \n", + "\n", + " Curricular units 2nd sem (without evaluations) Unemployment rate Inflation rate GDP TargetEncoded Previous qualification Bin \\\n", + "2524 0 15.5 2.8 -4.06 1 7 \n", + "3199 0 10.8 1.4 1.74 0 7 \n", + "4372 0 12.4 0.5 1.79 1 7 \n", + "4288 0 13.9 -0.3 0.79 1 8 \n", + "935 0 8.9 1.4 3.51 0 7 \n", + "\n", + " Mothers qualification Bin Fathers qualification Bin Marital Status Bin Nationality Bin Fathers occupation Bin \n", + "2524 7 6 1 1 5 \n", + "3199 7 7 1 1 9 \n", + "4372 7 7 1 1 9 \n", + "4288 7 9 1 1 2 \n", + "935 7 7 1 1 3 " + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.sample(5)" + ] + }, + { + "cell_type": "markdown", + "id": "VgLg2Is_hqJs", + "metadata": { + "id": "VgLg2Is_hqJs" + }, + "source": [ + "Look at the value count distribution again to confirm the new 200 value." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "8o-BqeMEhsrF", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 523 + }, + "id": "8o-BqeMEhsrF", + "outputId": "976bd8c3-fb45-4729-954a-07b9a0eb8bba" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Fathers occupation Bin\n", + "9 22.64\n", + "7 15.13\n", + "5 11.24\n", + "3 8.79\n", + "4 8.39\n", + "8 6.96\n", + "10 5.79\n", + "6 5.53\n", + "2 4.81\n", + "0 3.68\n", + "1 2.96\n", + "200 2.36\n", + "90 1.70\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Fathers occupation Bin'].value_counts(normalize=True, ascending=False).mul(100).round(2)" + ] + }, + { + "cell_type": "markdown", + "id": "S3-TvzAyh2KB", + "metadata": { + "id": "S3-TvzAyh2KB" + }, + "source": [ + "Now remove the original Father occupation column." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "Gna_Jr6Nh1S1", + "metadata": { + "id": "Gna_Jr6Nh1S1" + }, + "outputs": [], + "source": [ + "data.drop('Fathers occupation', axis=1, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "ZlQ7uXA6h55B", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 278 + }, + "id": "ZlQ7uXA6h55B", + "outputId": "e75caf7c-d89c-4881-c3c5-eb930a66389b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Application modeApplication orderCourseDaytime/evening attendance\\tPrevious qualification (grade)Mothers occupationAdmission gradeDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentInternationalCurricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Unemployment rateInflation rateGDPTargetEncodedPrevious qualification BinMothers qualification BinFathers qualification BinMarital Status BinNationality BinFathers occupation Bin
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" + ], + "text/plain": [ + " Application mode Application order Course Daytime/evening attendance\\t Previous qualification (grade) Mothers occupation \\\n", + "4020 1 1 9670 1 133.1 1 \n", + "347 39 1 9991 0 133.1 6 \n", + "2286 39 1 9991 0 120.0 6 \n", + "457 43 1 9853 1 140.0 9 \n", + "43 17 1 9773 1 120.0 5 \n", + "\n", + " Admission grade Displaced Educational special needs Debtor Tuition fees up to date Gender Scholarship holder Age at enrollment \\\n", + "4020 155.0 0 0 0 1 0 0 22 \n", + "347 136.2 0 0 0 1 0 0 39 \n", + "2286 160.0 0 0 0 1 1 0 40 \n", + "457 122.3 0 0 0 1 0 0 22 \n", + "43 121.4 0 0 0 0 0 0 20 \n", + "\n", + " International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) Curricular units 1st sem (evaluations) \\\n", + "4020 0 0 5 6 \n", + "347 0 0 5 6 \n", + "2286 0 0 5 5 \n", + "457 0 0 6 9 \n", + "43 0 0 6 7 \n", + "\n", + " Curricular units 1st sem (approved) Curricular units 1st sem (grade) Curricular units 1st sem (without evaluations) \\\n", + "4020 3 11.333333 0 \n", + "347 5 11.000000 0 \n", + "2286 5 11.000000 0 \n", + "457 5 13.600000 0 \n", + "43 4 13.000000 0 \n", + "\n", + " Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) Curricular units 2nd sem (evaluations) \\\n", + "4020 0 5 5 \n", + "347 0 5 9 \n", + "2286 0 5 5 \n", + "457 0 6 8 \n", + "43 0 6 7 \n", + "\n", + " Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) Curricular units 2nd sem (without evaluations) Unemployment rate \\\n", + "4020 0 0.000000 0 9.4 \n", + "347 5 11.666667 0 10.8 \n", + "2286 5 13.400000 0 15.5 \n", + "457 5 12.000000 0 12.7 \n", + "43 5 13.800000 0 12.4 \n", + "\n", + " Inflation rate GDP TargetEncoded Previous qualification Bin Mothers qualification Bin Fathers qualification Bin Marital Status Bin \\\n", + "4020 -0.8 -3.12 1 7 9 7 1 \n", + "347 1.4 1.74 0 7 3 3 4 \n", + "2286 2.8 -4.06 0 7 6 3 1 \n", + "457 3.7 -1.70 1 7 3 6 1 \n", + "43 0.5 1.79 1 7 7 5 1 \n", + "\n", + " Nationality Bin Fathers occupation Bin \n", + "4020 1 7 \n", + "347 1 6 \n", + "2286 1 8 \n", + "457 1 8 \n", + "43 1 5 " + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.sample(5)" + ] + }, + { + "cell_type": "markdown", + "id": "BZR6N4IYiGnj", + "metadata": { + "id": "BZR6N4IYiGnj" + }, + "source": [ + "Looks good. Now onto Mother's occupation column.\n", + "\n", + "Mother's Occupation\n", + "Using the same process, let's look again at the distribution of the Mothers occupation column using .value_counts descending from most to least frequency and the count of the distinct values." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "opTR0MB7iJjg", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "opTR0MB7iJjg", + "outputId": "99003528-2ed9-4bbe-d087-ecbc805d3fc6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total values: (32,)\n", + "Mothers occupation\n", + "9 35.28\n", + "4 17.78\n", + "5 12.05\n", + "3 7.74\n", + "2 7.08\n", + "7 6.13\n", + "0 4.20\n", + "1 2.41\n", + "90 2.00\n", + "6 1.86\n", + "8 0.92\n", + "99 0.65\n", + "191 0.43\n", + "194 0.20\n", + "123 0.20\n", + "141 0.13\n", + "10 0.12\n", + "175 0.12\n", + "193 0.10\n", + "134 0.10\n", + "144 0.10\n", + "192 0.08\n", + "151 0.05\n", + "132 0.05\n", + "143 0.05\n", + "153 0.03\n", + "152 0.03\n", + "122 0.03\n", + "125 0.02\n", + "173 0.02\n", + "131 0.02\n", + "171 0.02\n", + "Name: proportion, dtype: float64\n" + ] + } + ], + "source": [ + "mother_occupation_frequency_percentages = data['Mothers occupation'].value_counts(normalize=True, ascending=False).mul(100).round(2)\n", + "print('Total values:', mother_occupation_frequency_percentages.shape)\n", + "print(mother_occupation_frequency_percentages)" + ] + }, + { + "cell_type": "markdown", + "id": "RWwiTMuiiN5J", + "metadata": { + "id": "RWwiTMuiiN5J" + }, + "source": [ + "32 categories for Mothers occupation and similar to the Fathers occupation the majority of them are also less than 1% in frequency. How many are more than 1%? Filter to show only those values with more than 1% frequency." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "Dlv0oNYyiWtE", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Dlv0oNYyiWtE", + "outputId": "8f67ab18-5784-4702-ed14-84a576c3ed49" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mothers occupation\n", + "9 35.28\n", + "4 17.78\n", + "5 12.05\n", + "3 7.74\n", + "2 7.08\n", + "7 6.13\n", + "0 4.20\n", + "1 2.41\n", + "90 2.00\n", + "6 1.86\n", + "Name: proportion, dtype: float64\n", + "Total categories over 1 percent frequeancy: (10,)\n" + ] + } + ], + "source": [ + "print(mother_occupation_frequency_percentages[mother_occupation_frequency_percentages > 1])\n", + "print('Total categories over 1 percent frequeancy:', mother_occupation_frequency_percentages[mother_occupation_frequency_percentages > 1].shape)" + ] + }, + { + "cell_type": "markdown", + "id": "238DrOTlidvk", + "metadata": { + "id": "238DrOTlidvk" + }, + "source": [ + "Out of the 32 categories, 10 of them are over 1% frequency. We can bin down to 11 categories, keeping the 10 that are over 1% and the rest will be bundled into *Other*, just like all the pervious categorical variables have been binned.\n", + "\n", + "| Mothers Occupation | Percentage Frequency | New Bin Value | Description |\n", + "|---------------------------------------------------------------------------------------------------------------------------|----------------------|---------------|--------------------------------------------------------------------------------------------------|\n", + "| 9 - Unskilled Workers | 35.28% | 9 | Unskilled Workers |\n", + "| 4 - Administrative staff | 17.78% | 4 | Administrative staff |\n", + "| 5 - Personal Services, Security and Safety Workers and Sellers | 12.05% | 5 | Personal Services, Security and Safety Workers and Sellers |\n", + "| 3 - Intermediate Level Technicians and Professions | 7.74% | 3 | Intermediate Level Technicians and Professions |\n", + "| 2 - Specialists in Intellectual and Scientific Activities | 7.08% | 2 | Specialists in Intellectual and Scientific Activities |\n", + "| 7 - Skilled Workers in Industry, Construction and Craftsmen | 6.13% | 7 | Skilled Workers in Industry, Construction and Craftsmen |\n", + "| 0 - Student | 4.20% | 0 | Student |\n", + "| 1 - Representatives of the Legislative Power and Executive Bodies, Directors, Directors and Executive Managers | 2.41% | 1 | Representatives of the Legislative Power and Executive Bodies, Directors, Directors and Executive Managers |\n", + "| 90 - Other Situation | 2.00% | 90 | Other Situation |\n", + "| 6 - Farmers and Skilled Workers in Agriculture, Fisheries and Forestry | 1.86% | 6 | Farmers and Skilled Workers in Agriculture, Fisheries and Forestry |\n", + "| All other values not in the list above | | 200 | Other (New Bundle) |\n", + "\n", + "\n", + "\n", + "All the other categories not on this list will be set to a new value called *Other* which will be different from the value *90 - Other Situation*. We'll use an integer higher than the maximum value of this variable which we alread determined when binning the *Fathers occupation* column. We'll be using the value 200 for *Other*. \n", + "\n", + "Identify those over 1% and store in a variable called mother_occupation_condition.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "lb2exuQJil20", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lb2exuQJil20", + "outputId": "20f11048-db2a-4204-b5bb-474f78b4fa94" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[9, 4, 5, 3, 2, 7, 0, 1, 90, 6]\n" + ] + } + ], + "source": [ + "print(mother_occupation_frequency_percentages[mother_occupation_frequency_percentages > 1].index.tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "RphtvPVxiqx4", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RphtvPVxiqx4", + "outputId": "ab76ac36-572a-454b-9859-b141aefbfe7e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mothers occupation\n", + "True 5798\n", + "False 208\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "mother_occupation_condition = data['Mothers occupation'].isin([9, 4, 5, 3, 2, 7, 0, 1, 6, 90])\n", + "print(mother_occupation_condition.value_counts())" + ] + }, + { + "cell_type": "markdown", + "id": "Ke-HITaxizw7", + "metadata": { + "id": "Ke-HITaxizw7" + }, + "source": [ + "Update the data to set to *200* for *Other* on those not in the mother_occupation_condition list." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "foi4iMt2i5Jl", + "metadata": { + "id": "foi4iMt2i5Jl" + }, + "outputs": [], + "source": [ + "data['Mothers occupation Bin'] = np.where(mother_occupation_condition, data['Mothers occupation'], 200) # Setting other values to 200" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "Wv8EoqHgi7FT", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 278 + }, + "id": "Wv8EoqHgi7FT", + "outputId": "45aaba3b-09ff-4685-d563-99e41c360b43" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Application mode Application order Course Daytime/evening attendance\\t Previous qualification (grade) Mothers occupation \\\n", + "3135 1 1 171 1 138.0 4 \n", + "618 17 2 9500 1 137.0 4 \n", + "728 1 2 171 1 131.0 4 \n", + "3398 1 5 9500 1 130.0 4 \n", + "3865 17 2 9773 1 150.0 4 \n", + "\n", + " Admission grade Displaced Educational special needs Debtor Tuition fees up to date Gender Scholarship holder Age at enrollment \\\n", + "3135 149.9 1 0 0 1 0 0 20 \n", + "618 126.3 1 0 0 1 0 1 19 \n", + "728 131.4 1 0 0 1 1 0 20 \n", + "3398 122.3 0 0 0 1 0 0 19 \n", + "3865 130.8 0 0 0 1 1 0 20 \n", + "\n", + " International Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) Curricular units 1st sem (evaluations) \\\n", + "3135 0 0 0 0 \n", + "618 0 0 7 9 \n", + "728 1 0 0 0 \n", + "3398 0 0 7 7 \n", + "3865 0 0 6 15 \n", + "\n", + " Curricular units 1st sem (approved) Curricular units 1st sem (grade) Curricular units 1st sem (without evaluations) \\\n", + "3135 0 0.00 0 \n", + "618 6 12.34 0 \n", + "728 0 0.00 0 \n", + "3398 6 14.35 0 \n", + "3865 2 12.00 0 \n", + "\n", + " Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) Curricular units 2nd sem (evaluations) \\\n", + "3135 0 0 0 \n", + "618 0 8 9 \n", + "728 0 0 0 \n", + "3398 0 7 7 \n", + "3865 0 6 12 \n", + "\n", + " Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) Curricular units 2nd sem (without evaluations) Unemployment rate \\\n", + "3135 0 0.00000 0 11.1 \n", + "618 8 13.01875 0 11.1 \n", + "728 0 0.00000 0 7.6 \n", + "3398 6 14.35000 0 7.6 \n", + "3865 0 0.00000 0 10.8 \n", + "\n", + " Inflation rate GDP TargetEncoded Previous qualification Bin Mothers qualification Bin Fathers qualification Bin Marital Status Bin \\\n", + "3135 0.6 2.02 0 7 7 7 1 \n", + "618 0.6 2.02 0 7 6 3 1 \n", + "728 2.6 0.32 0 7 6 7 1 \n", + "3398 2.6 0.32 0 7 7 6 2 \n", + "3865 1.4 1.74 1 7 7 7 1 \n", + "\n", + " Nationality Bin Fathers occupation Bin Mothers occupation Bin \n", + "3135 1 3 4 \n", + "618 1 7 4 \n", + "728 0 7 4 \n", + "3398 1 4 4 \n", + "3865 1 5 4 " + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.sample(5)" + ] + }, + { + "cell_type": "markdown", + "id": "dUhYeHxFjDOU", + "metadata": { + "id": "dUhYeHxFjDOU" + }, + "source": [ + "Check the value count again and look for the new *200* value." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "Y9ImUK5wjFdh", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 460 + }, + "id": "Y9ImUK5wjFdh", + "outputId": "d63cb49a-ee76-4e65-d55d-68808292c5e4" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Mothers occupation Bin\n", + "9 35.28\n", + "4 17.78\n", + "5 12.05\n", + "3 7.74\n", + "2 7.08\n", + "7 6.13\n", + "0 4.20\n", + "200 3.46\n", + "1 2.41\n", + "90 2.00\n", + "6 1.86\n", + "Name: proportion, dtype: float64" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Mothers occupation Bin'].value_counts(normalize=True, ascending=False).mul(100).round(2)" + ] + }, + { + "cell_type": "markdown", + "id": "KKNoH_lHjKxS", + "metadata": { + "id": "KKNoH_lHjKxS" + }, + "source": [ + "Remove the original column and check." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "ooX0HsbAjKaA", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 278 + }, + "id": "ooX0HsbAjKaA", + "outputId": "6047662a-7ae8-4354-e314-46aae2874256" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Application modeApplication orderCourseDaytime/evening attendance\\tPrevious qualification (grade)Admission gradeDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentInternationalCurricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Unemployment rateInflation rateGDPTargetEncodedPrevious qualification BinMothers qualification BinFathers qualification BinMarital Status BinNationality BinFathers occupation BinMothers occupation Bin
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" + ], + "text/plain": [ + " Application mode Application order Course Daytime/evening attendance\\t Previous qualification (grade) Admission grade Displaced \\\n", + "3996 1 1 9853 1 128.0 120.0 1 \n", + "2904 39 1 8014 0 133.1 129.0 0 \n", + "963 1 2 9119 1 149.0 141.0 0 \n", + "483 44 1 9085 1 140.0 140.0 0 \n", + "2012 39 1 9853 1 133.1 105.5 0 \n", + "\n", + " Educational special needs Debtor Tuition fees up to date Gender Scholarship holder Age at enrollment International \\\n", + "3996 0 0 1 0 0 18 0 \n", + "2904 0 0 1 0 0 37 0 \n", + "963 0 0 1 1 0 18 0 \n", + "483 0 0 1 0 0 20 0 \n", + "2012 0 1 1 0 0 33 0 \n", + "\n", + " Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) Curricular units 1st sem (evaluations) \\\n", + "3996 0 6 11 \n", + "2904 0 6 13 \n", + "963 0 5 9 \n", + "483 2 6 9 \n", + "2012 0 6 7 \n", + "\n", + " Curricular units 1st sem (approved) Curricular units 1st sem (grade) Curricular units 1st sem (without evaluations) \\\n", + "3996 3 12.000000 0 \n", + "2904 4 12.000000 0 \n", + "963 2 11.666667 0 \n", + "483 6 12.666667 0 \n", + "2012 5 13.200000 0 \n", + "\n", + " Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) Curricular units 2nd sem (evaluations) \\\n", + "3996 0 6 13 \n", + "2904 0 6 10 \n", + "963 0 5 9 \n", + "483 1 6 12 \n", + "2012 0 6 7 \n", + "\n", + " Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) Curricular units 2nd sem (without evaluations) Unemployment rate \\\n", + "3996 2 12.0 0 12.7 \n", + "2904 3 12.0 0 16.2 \n", + "963 2 12.5 2 7.6 \n", + "483 2 13.0 0 13.9 \n", + "2012 5 10.6 0 7.6 \n", + "\n", + " Inflation rate GDP TargetEncoded Previous qualification Bin Mothers qualification Bin Fathers qualification Bin Marital Status Bin \\\n", + "3996 3.7 -1.70 1 7 7 7 1 \n", + "2904 0.3 -0.92 1 7 3 3 1 \n", + "963 2.6 0.32 0 7 5 5 1 \n", + "483 -0.3 0.79 0 8 5 5 1 \n", + "2012 2.6 0.32 1 6 5 7 7 \n", + "\n", + " Nationality Bin Fathers occupation Bin Mothers occupation Bin \n", + "3996 1 9 9 \n", + "2904 1 7 9 \n", + "963 1 5 5 \n", + "483 1 7 9 \n", + "2012 1 3 5 " + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.drop('Mothers occupation', axis=1, inplace=True)\n", + "data.sample(5)" + ] + }, + { + "cell_type": "markdown", + "id": "Qc_1pB3LjXgD", + "metadata": { + "id": "Qc_1pB3LjXgD" + }, + "source": [ + "Looks good. Next categorical variable on the list.\n", + "\n", + "- Application mode\n", + "- Application order\n", + "- Course\n", + "- ~~Fathers occupation~~\n", + "- ~~Mothers occupation~~\n", + "- ~~Nationality~~\n", + "- ~~Marital status~~\n", + "- ~~Previous qualification~~\n", + "- ~~Fathers qualification~~\n", + "- ~~Mothers qualification~~\n", + "\n", + "\n", + "\n", + "Onto the *Application mode* column now." + ] + }, + { + "cell_type": "markdown", + "id": "7qfhCs96jYz5", + "metadata": { + "id": "7qfhCs96jYz5" + }, + "source": [ + "## 13. Application Mode Variable\n", + "\n", + "#### Binning Application Mode Variable\n", + "\n", + "Let's breakdown the different values and get a frequency percentage for each." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "lRFJqTAWjguv", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lRFJqTAWjguv", + "outputId": "c12ae213-bdf7-4244-ef03-06684a194229" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total values: (17,)\n", + "Application mode\n", + "1 34.32\n", + "39 20.53\n", + "17 19.55\n", + "43 7.34\n", + "44 5.13\n", + "7 3.90\n", + "18 3.00\n", + "42 2.10\n", + "51 1.38\n", + "16 0.78\n", + "15 0.70\n", + "53 0.63\n", + "5 0.28\n", + "10 0.25\n", + "27 0.07\n", + "2 0.03\n", + "57 0.02\n", + "Name: proportion, dtype: float64\n" + ] + } + ], + "source": [ + "application_mode_frequency_percentages = data['Application mode'].value_counts(normalize=True, ascending=False).mul(100).round(2)\n", + "print('Total values:', application_mode_frequency_percentages.shape)\n", + "print(application_mode_frequency_percentages)" + ] + }, + { + "cell_type": "markdown", + "id": "NVAuCMDtjlgX", + "metadata": { + "id": "NVAuCMDtjlgX" + }, + "source": [ + "Which ones are over 1%?" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "ESWF8HQXjowg", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ESWF8HQXjowg", + "outputId": "7258ee73-97fb-42a6-8b48-66142bc399a0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Application mode\n", + "1 34.32\n", + "39 20.53\n", + "17 19.55\n", + "43 7.34\n", + "44 5.13\n", + "7 3.90\n", + "18 3.00\n", + "42 2.10\n", + "51 1.38\n", + "Name: proportion, dtype: float64\n", + "Total categories over 1 percent frequeancy: (9,)\n" + ] + } + ], + "source": [ + "print(application_mode_frequency_percentages[application_mode_frequency_percentages > 1])\n", + "print('Total categories over 1 percent frequeancy:', application_mode_frequency_percentages[application_mode_frequency_percentages > 1].shape)" + ] + }, + { + "cell_type": "markdown", + "id": "pxQHqPldj465", + "metadata": { + "id": "pxQHqPldj465" + }, + "source": [ + "Looks like about half (9 out of 17) of the categories in the *Application mode* column are below 1% in precentage frequency. We'll use the same approach here and bundle all values below 1% into *Other* while the rest will stay in their own bin.\n", + "\n", + "We can bin the 17 categories down to 10 categories, keeping the 10 that are over 1% and the rest will be bundled into *Other*, just like all the pervious categorical variables have been binned.\n", + "\n", + "According to the [Data Dictionary](#Data-dictionary), the maximum value for *Application mode* is *57 - Change of institution/course (International)*. But I do notice that the *0* is not used so we'll go with the *0* value to use as the place holder for the bundled new *Other* value.\n", + "\n", + "\n", + "| Application Mode | Percentage Frequency | New Bin Value | Description |\n", + "|----------------------------------------------------------------|----------------------|---------------|-----------------------------------------------|\n", + "| 1 - 1st phase - general contingent | 34.32% | 1 | 1st phase - general contingent |\n", + "| 39 - Over 23 years old | 20.53% | 39 | Over 23 years old |\n", + "| 17 - 2nd phase - general contingent | 19.55% | 17 | 2nd phase - general contingent |\n", + "| 43 - Change of course | 7.34% | 43 | Change of course |\n", + "| 44 - Technological specialization diploma holders | 5.13% | 44 | Technological specialization diploma holders |\n", + "| 7 - Holders of other higher courses | 3.90% | 7 | Holders of other higher courses |\n", + "| 18 - 3rd phase - general contingent | 3.00% | 18 | 3rd phase - general contingent |\n", + "| 42 - Transfer | 2.10% | 42 | Transfer |\n", + "| 51 - Change of institution/course | 1.38% | 51 | Change of institution/course |\n", + "| All other values not in the list above | | 0 | Other (New Bundle) |\n", + "\n", + "\n", + "\n", + "All the other categories not on this list will be set to a new value *0* called *Other*." + ] + }, + { + "cell_type": "markdown", + "id": "sT_RCwvij9iZ", + "metadata": { + "id": "sT_RCwvij9iZ" + }, + "source": [ + "List the values of the ones over 1%." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "Uq9QPyxBj-35", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Uq9QPyxBj-35", + "outputId": "a9100013-b3b5-4219-db7c-8a9951637689" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 39, 17, 43, 44, 7, 18, 42, 51]\n" + ] + } + ], + "source": [ + "print(application_mode_frequency_percentages[application_mode_frequency_percentages > 1].index.tolist())\n" + ] + }, + { + "cell_type": "markdown", + "id": "uUqVRCq6kFHw", + "metadata": { + "id": "uUqVRCq6kFHw" + }, + "source": [ + "Store these values in a variable called application_mode_condition.\n", + "Print the distribution of those in the list from the data." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "60PGAs6mkGHu", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "60PGAs6mkGHu", + "outputId": "e3f8694d-c0e1-4d88-e904-641e5606f04d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Application mode\n", + "True 5840\n", + "False 166\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "application_mode_condition = data['Application mode'].isin([1, 17, 39, 43, 44, 7, 18, 42, 51])\n", + "print(application_mode_condition.value_counts())" + ] + }, + { + "cell_type": "markdown", + "id": "As3ka462kOOh", + "metadata": { + "id": "As3ka462kOOh" + }, + "source": [ + "Update the data to include a new column and set the values to be the same as those in the above list of over 1% values and the rest of the rows set it to 0." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "phZyFhuckPyy", + "metadata": { + "id": "phZyFhuckPyy" + }, + "outputs": [], + "source": [ + "data['Application mode Bin'] = np.where(application_mode_condition, data['Application mode'], 0) # Setting other values to 0" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "CoOAQAG9kUHQ", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 278 + }, + "id": "CoOAQAG9kUHQ", + "outputId": "2efc1182-fad7-45ee-8671-3b8141d2ac4f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Next is *Course*.\n", + "\n", + "- ~~Application mode~~\n", + "- Application order\n", + "- Course\n", + "- ~~Fathers occupation~~\n", + "- ~~Mothers occupation~~\n", + "- ~~Nationality~~\n", + "- ~~Marital status~~\n", + "- ~~Previous qualification~~\n", + "- ~~Fathers qualification~~\n", + "- ~~Mothers qualification~~\n" + ] + }, + { + "cell_type": "markdown", + "id": "dRSxQZF4k6y-", + "metadata": { + "id": "dRSxQZF4k6y-" + }, + "source": [ + "## Course Variable\n", + "\n", + "#### Binning Course Variable\n", + "\n", + "Let's look at the distribution of the value counts here." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "I7bfHzvImh8M", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "I7bfHzvImh8M", + "outputId": "f9a50289-499c-4af9-c65f-639492b16d74" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total values: (17,)\n", + "Course\n", + "9500 15.03\n", + "9147 8.39\n", + "9085 7.44\n", + "9773 7.19\n", + "9238 6.88\n", + "9991 6.66\n", + "9670 6.36\n", + "9254 6.16\n", + "9070 5.24\n", + "9003 5.14\n", + "171 5.00\n", + "9853 4.88\n", + "8014 4.85\n", + "9119 4.70\n", + "9130 3.93\n", + "9556 1.86\n", + "33 0.28\n", + "Name: proportion, dtype: float64\n" + ] + } + ], + "source": [ + "course_frequency_percentages = data['Course'].value_counts(normalize=True, ascending=False).mul(100).round(2)\n", + "print('Total values:', course_frequency_percentages.shape)\n", + "print(course_frequency_percentages)" + ] + }, + { + "cell_type": "markdown", + "id": "NEc1SjDymsB6", + "metadata": { + "id": "NEc1SjDymsB6" + }, + "source": [ + "Let's plot it out visually and print a sample.\n", + "\n", + "> Note: the lowest frequency above is Course 33 which also was pointed out earlier when I plotted the percenage of Target values within certain feature category values that appear to have a high rate of drop out. This proves that the high percentage was occuring from a very low population." + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "38_iKEKOmxUb", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 905 + }, + "id": "38_iKEKOmxUb", + "outputId": "438aa2b1-4bf7-4ce8-b105-882c92f8f55d" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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Application orderCourseDaytime/evening attendance\\tPrevious qualification (grade)Admission gradeDisplacedEducational special needsDebtorTuition fees up to dateGenderScholarship holderAge at enrollmentInternationalCurricular units 1st sem (credited)Curricular units 1st sem (enrolled)Curricular units 1st sem (evaluations)Curricular units 1st sem (approved)Curricular units 1st sem (grade)Curricular units 1st sem (without evaluations)Curricular units 2nd sem (credited)Curricular units 2nd sem (enrolled)Curricular units 2nd sem (evaluations)Curricular units 2nd sem (approved)Curricular units 2nd sem (grade)Curricular units 2nd sem (without evaluations)Unemployment rateInflation rateGDPTargetEncodedPrevious qualification BinMothers qualification BinFathers qualification BinMarital Status BinNationality BinFathers occupation BinMothers occupation BinApplication mode Bin
936195001133.1114.80001002800715110.75000000710110.000000013.9-0.30.791655119939
3620190851133.1109.60001001810613412.25000000615511.000000011.10.62.02173310990
1498190031140.0150.00001102800612512.00000000610612.333333011.10.62.020733418939
415190851131.0137.0000100180055514.0000000056515.40000007.62.60.3207331190901
953595001137.0129.300010019007000.000000008000.00000009.4-0.8-3.1217671110118
3448197731140.0135.8100101190066613.0000000067613.000000010.81.41.74076311691
3642280140133.1118.0000101200066611.66666700612514.000000010.81.41.740767111117
2282192541133.1121.0001010210069511.8333330068510.00000008.91.43.51175711991
1062180140130.0131.5000100270068511.0000000067611.166667011.10.62.0207351110939
3168192541129.0126.2001001210067613.4285710068611.33333308.91.43.51177711241
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" + ], + "text/plain": [ + " Application order Course Daytime/evening attendance\\t Previous qualification (grade) Admission grade Displaced \\\n", + "936 1 9500 1 133.1 114.8 0 \n", + "3620 1 9085 1 133.1 109.6 0 \n", + "1498 1 9003 1 140.0 150.0 0 \n", + "415 1 9085 1 131.0 137.0 0 \n", + "953 5 9500 1 137.0 129.3 0 \n", + "3448 1 9773 1 140.0 135.8 1 \n", + "3642 2 8014 0 133.1 118.0 0 \n", + "2282 1 9254 1 133.1 121.0 0 \n", + "1062 1 8014 0 130.0 131.5 0 \n", + "3168 1 9254 1 129.0 126.2 0 \n", + "\n", + " Educational special needs Debtor Tuition fees up to date Gender Scholarship holder Age at enrollment International \\\n", + "936 0 0 1 0 0 28 0 \n", + "3620 0 0 1 0 0 18 1 \n", + "1498 0 0 1 1 0 28 0 \n", + "415 0 0 1 0 0 18 0 \n", + "953 0 0 1 0 0 19 0 \n", + "3448 0 0 1 0 1 19 0 \n", + "3642 0 0 1 0 1 20 0 \n", + "2282 0 1 0 1 0 21 0 \n", + "1062 0 0 1 0 0 27 0 \n", + "3168 0 1 0 0 1 21 0 \n", + "\n", + " Curricular units 1st sem (credited) Curricular units 1st sem (enrolled) Curricular units 1st sem (evaluations) \\\n", + "936 0 7 15 \n", + "3620 0 6 13 \n", + "1498 0 6 12 \n", + "415 0 5 5 \n", + "953 0 7 0 \n", + "3448 0 6 6 \n", + "3642 0 6 6 \n", + "2282 0 6 9 \n", + "1062 0 6 8 \n", + "3168 0 6 7 \n", + "\n", + " Curricular units 1st sem (approved) Curricular units 1st sem (grade) Curricular units 1st sem (without evaluations) \\\n", + "936 1 10.750000 0 \n", + "3620 4 12.250000 0 \n", + "1498 5 12.000000 0 \n", + "415 5 14.000000 0 \n", + "953 0 0.000000 0 \n", + "3448 6 13.000000 0 \n", + "3642 6 11.666667 0 \n", + "2282 5 11.833333 0 \n", + "1062 5 11.000000 0 \n", + "3168 6 13.428571 0 \n", + "\n", + " Curricular units 2nd sem (credited) Curricular units 2nd sem (enrolled) Curricular units 2nd sem (evaluations) \\\n", + "936 0 7 10 \n", + "3620 0 6 15 \n", + "1498 0 6 10 \n", + "415 0 5 6 \n", + "953 0 8 0 \n", + "3448 0 6 7 \n", + "3642 0 6 12 \n", + "2282 0 6 8 \n", + "1062 0 6 7 \n", + "3168 0 6 8 \n", + "\n", + " Curricular units 2nd sem (approved) Curricular units 2nd sem (grade) Curricular units 2nd sem (without evaluations) Unemployment rate \\\n", + "936 1 10.000000 0 13.9 \n", + "3620 5 11.000000 0 11.1 \n", + "1498 6 12.333333 0 11.1 \n", + "415 5 15.400000 0 7.6 \n", + "953 0 0.000000 0 9.4 \n", + "3448 6 13.000000 0 10.8 \n", + "3642 5 14.000000 0 10.8 \n", + "2282 5 10.000000 0 8.9 \n", + "1062 6 11.166667 0 11.1 \n", + "3168 6 11.333333 0 8.9 \n", + "\n", + " Inflation rate GDP TargetEncoded Previous qualification Bin Mothers qualification Bin Fathers qualification Bin Marital Status Bin \\\n", + "936 -0.3 0.79 1 6 5 5 1 \n", + "3620 0.6 2.02 1 7 3 3 1 \n", + "1498 0.6 2.02 0 7 3 3 4 \n", + "415 2.6 0.32 0 7 3 3 1 \n", + "953 -0.8 -3.12 1 7 6 7 1 \n", + "3448 1.4 1.74 0 7 6 3 1 \n", + "3642 1.4 1.74 0 7 6 7 1 \n", + "2282 1.4 3.51 1 7 5 7 1 \n", + "1062 0.6 2.02 0 7 3 5 1 \n", + "3168 1.4 3.51 1 7 7 7 1 \n", + "\n", + " Nationality Bin Fathers occupation Bin Mothers occupation Bin Application mode Bin \n", + "936 1 9 9 39 \n", + "3620 0 9 9 0 \n", + "1498 1 8 9 39 \n", + "415 1 90 90 1 \n", + "953 1 10 1 18 \n", + "3448 1 6 9 1 \n", + "3642 1 1 1 17 \n", + "2282 1 9 9 1 \n", + "1062 1 10 9 39 \n", + "3168 1 2 4 1 " + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sns.histplot(data['Course'])\n", + "\n", + "# Include median and mean lines\n", + "plt.axvline(data['Course'].mean(), c='blue', label='Mean')\n", + "plt.axvline(data['Course'].median(), c='green', label='Median')\n", + "plt.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "data.sample(10)" + ] + }, + { + "cell_type": "markdown", + "id": "lw67ifc-m8m0", + "metadata": { + "id": "lw67ifc-m8m0" + }, + "source": [ + "From viewing the distribution of the Course column and seeing how the courses are mostly over 3%, I decided to keep this column as is. Since we've decreased the cardinality of most of the other high cardinality columns at this point keeping all the distinct values for Course column will not widen the dataset so much when we do the one-hot encoding.\n", + "\n", + "The same goes for the next and last categorical column, Application order. This one seems like an ordinal categorical numerical value. Much like Age. We will also keep this column as is. " + ] + }, + { + "cell_type": "markdown", + "id": "yjRgo_N_nVJn", + "metadata": { + "id": "yjRgo_N_nVJn" + }, + "source": [ + "# One-Hot Encoding\n", + "Since now we have minimized the cardinality of most of the categorical columns we will convert all the categorical features into dummy variables. \n", + "\n", + "These columns are:\n", + "- Marital Status\n", + "- Application mode\n", + "- Application order\n", + "- Course\n", + "- Previous qualification\n", + "- Nationality\n", + "- Mother's qualification\n", + "- Father's qualification\n", + "- Mother's occupation\n", + "- Father's occupation\n", + "\n", + "\n", + "Because of the high cardinality (large number of unique values in each) of these columns it may not provide much predictive power in machine learning models and can lead to overfitting. There are a few strategies to overcome this such as binning which we had done except for two columns (*Appilcation order* and *Cousre*). But for now let's convert them to one-hot encoded features and see how it models.\n", + "\n", + "I'll do this in a loop. But since these column names are long I'll create a dictionary containing these columns to convert and the prefix name to use.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "vWv0Vb4ix6Ai", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vWv0Vb4ix6Ai", + "outputId": "ed50ef77-d415-4289-e267-e1e9ffc2248e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(6006, 37)" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check the current shape of data\n", + "data.shape" + ] + }, + { + "cell_type": "markdown", + "id": "m9k8LAiwyNDt", + "metadata": { + "id": "m9k8LAiwyNDt" + }, + "source": [ + "Create a list for all the columns to be set to dummy variables. Then loop through the list the to address each column, setting the dummy variables and dropping one of the values. Afterwards the original column will be dropped. Display the shape after it all." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "juUntE65ySpy", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "juUntE65ySpy", + "outputId": "6ab0af59-6a82-442c-db28-4f8043787f78" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(6006, 105)" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Dictionary mapping column names to prefixes\n", + "column_prefixes = {'Marital Status Bin': 'Marital_',\n", + " 'Application mode Bin': 'AppMode_',\n", + " 'Application order': 'AppOrder_',\n", + " 'Course': 'Course_',\n", + " 'Previous qualification Bin': 'PrevQual_',\n", + " 'Nationality Bin': 'Nation_',\n", + " 'Mothers qualification Bin': 'MotherQual_',\n", + " 'Fathers qualification Bin': 'FatherQual_',\n", + " 'Mothers occupation Bin': 'MotherOcc_',\n", + " 'Fathers occupation Bin': 'FatherOcc_'}\n", + "\n", + "# Loop through each column and perform one-hot encoding with the specified prefix\n", + "for column, prefix in column_prefixes.items():\n", + "\n", + " # Perform one-hot encoding\n", + " one_hot_encoded_df = pd.get_dummies(data[column], prefix=prefix)\n", + "\n", + " # Drop one dummy variable (the one with the least frequency value)\n", + " least_freq_dummy = one_hot_encoded_df.sum().idxmin()\n", + " one_hot_encoded_df.drop(columns=[least_freq_dummy], inplace=True)\n", + "\n", + " # Concatenate the one-hot encoded DataFrame with the original DataFrame\n", + " data = pd.concat([data, one_hot_encoded_df], axis=1)\n", + "\n", + " # Drop the original categorical column\n", + " data.drop(columns=[column], inplace=True)\n", + "\n", + "\n", + " # check the shape of data now\n", + "data.shape" + ] + }, + { + "cell_type": "markdown", + "id": "9uEvsOkoyawT", + "metadata": { + "id": "9uEvsOkoyawT" + }, + "source": [ + "There are now 105 features in the dataframe including the Target variable and 6006 instances. 105 is not too much considering we kept the Course and Application order columns as is without binning. Let's confirm the changes by looking at the data and then we can start modeling." + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "Nadm9UtIyiJb", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 278 + }, + "id": "Nadm9UtIyiJb", + "outputId": "29e14c36-c73e-405c-b2ef-aa92a96df5b6" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Daytime/evening attendance\\t Previous qualification (grade) Admission grade Displaced Educational special needs Debtor \\\n", + "1 1 160.0 142.5 1 0 0 \n", + "3 1 122.0 119.6 1 0 0 \n", + "4 0 100.0 141.5 0 0 0 \n", + "5 0 133.1 114.8 0 0 1 \n", + "6 1 142.0 128.4 1 0 0 \n", + "\n", + " Tuition fees up to date Gender Scholarship holder Age at enrollment International Curricular units 1st sem (credited) \\\n", + "1 0 1 0 19 0 0 \n", + "3 1 0 0 20 0 0 \n", + "4 1 0 0 45 0 0 \n", + "5 1 1 0 50 0 0 \n", + "6 1 0 1 18 0 0 \n", + "\n", + " Curricular units 1st sem (enrolled) Curricular units 1st sem (evaluations) Curricular units 1st sem (approved) \\\n", + "1 6 6 6 \n", + "3 6 8 6 \n", + "4 6 9 5 \n", + "5 5 10 5 \n", + "6 7 9 7 \n", + "\n", + " Curricular units 1st sem (grade) Curricular units 1st sem (without evaluations) Curricular units 2nd sem (credited) \\\n", + "1 14.000000 0 0 \n", + "3 13.428571 0 0 \n", + "4 12.333333 0 0 \n", + "5 11.857143 0 0 \n", + "6 13.300000 0 0 \n", + "\n", + " Curricular units 2nd sem (enrolled) Curricular units 2nd sem (evaluations) Curricular units 2nd sem (approved) \\\n", + "1 6 6 6 \n", + "3 6 10 5 \n", + "4 6 6 6 \n", + "5 5 17 5 \n", + "6 8 8 8 \n", + "\n", + " Curricular units 2nd sem (grade) Curricular units 2nd sem (without evaluations) Unemployment rate Inflation rate GDP TargetEncoded \\\n", + "1 13.666667 0 13.9 -0.3 0.79 0 \n", + "3 12.400000 0 9.4 -0.8 -3.12 0 \n", + "4 13.000000 0 13.9 -0.3 0.79 0 \n", + "5 11.500000 5 16.2 0.3 -0.92 0 \n", + "6 14.345000 0 15.5 2.8 -4.06 0 \n", + "\n", + " Marital__1 Marital__2 Marital__4 AppMode__0 AppMode__1 AppMode__7 AppMode__17 AppMode__18 AppMode__39 AppMode__42 AppMode__43 \\\n", + "1 True False False True False False False False False False False \n", + "3 True False False False False False True False False False False \n", + "4 False True False False False False False False True False False \n", + "5 False True False False False False False False True False False \n", + "6 True False False False True False False False False False False \n", + "\n", + " AppMode__44 AppOrder__1 AppOrder__2 AppOrder__3 AppOrder__4 AppOrder__5 AppOrder__6 AppOrder__9 Course__171 Course__8014 Course__9003 \\\n", + "1 False True False False False False False False False False False \n", + "3 False False True False False False False False False False False \n", + "4 False True False False False False False False False True False \n", + "5 False True False False False False False False False False False \n", + "6 False True False False False False False False False False False \n", + "\n", + " Course__9070 Course__9085 Course__9119 Course__9130 Course__9147 Course__9238 Course__9254 Course__9500 Course__9556 Course__9670 \\\n", + "1 False False False False False False True False False False \n", + "3 False False False False False False False False False False \n", + "4 False False False False False False False False False False \n", + "5 False False False False False False False False False False \n", + "6 False False False False False False False True False False \n", + "\n", + " Course__9773 Course__9853 Course__9991 PrevQual__6 PrevQual__7 PrevQual__8 PrevQual__9 Nation__1 MotherQual__1 MotherQual__2 \\\n", + "1 False False False False True False False True False False \n", + "3 True False False False True False False True False False \n", + "4 False False False False True False False True False False \n", + "5 False False True True False False False True False False \n", + "6 False False False False True False False True False False \n", + "\n", + " MotherQual__3 MotherQual__5 MotherQual__6 MotherQual__7 MotherQual__8 MotherQual__9 FatherQual__1 FatherQual__2 FatherQual__3 \\\n", + "1 False False False True False False False False False \n", + "3 False True False False False False False False True \n", + "4 True False False False False False False False False \n", + "5 True False False False False False False False True \n", + "6 False False True False False False False False False \n", + "\n", + " FatherQual__5 FatherQual__6 FatherQual__7 FatherQual__8 FatherQual__9 MotherOcc__0 MotherOcc__1 MotherOcc__2 MotherOcc__3 MotherOcc__4 \\\n", + "1 False False False False True False False False True False \n", + "3 False False False False False False False False False False \n", + "4 True False False False False False False False False False \n", + "5 False False False False False False False False False False \n", + "6 True False False False False False False False False False \n", + "\n", + " MotherOcc__5 MotherOcc__7 MotherOcc__9 MotherOcc__90 MotherOcc__200 FatherOcc__0 FatherOcc__1 FatherOcc__2 FatherOcc__3 FatherOcc__4 \\\n", + "1 False False False False False False False False True False \n", + "3 True False False False False False False False True False \n", + "4 False False True False False False False False False False \n", + "5 False False True False False False False False False False \n", + "6 False True False False False False False False False False \n", + "\n", + " FatherOcc__5 FatherOcc__6 FatherOcc__7 FatherOcc__8 FatherOcc__9 FatherOcc__10 FatherOcc__200 \n", + "1 False False False False False False False \n", + "3 False False False False False False False \n", + "4 False False False False True False False \n", + "5 False False True False False False False \n", + "6 False False False False False True False " + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "iC_fM4nsy0K8", + "metadata": { + "id": "iC_fM4nsy0K8" + }, + "source": [ + "# Modeling\n", + "# Logistic Regression\n", + "To get a baseline let's fit this into a logistic model.\n", + "\n", + "### Target Data Split\n", + "Split the data between target and independent variables and then split between training and test data." + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "zid6eVwXzACy", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zid6eVwXzACy", + "outputId": "d327d523-bf9f-4c18-d3d5-4195b388116d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X shape (6006, 104)\n", + "y shape (6006,)\n" + ] + } + ], + "source": [ + "# Split the data between independent and dependent variables\n", + "X = data.drop('TargetEncoded', axis=1)\n", + "y = data['TargetEncoded']\n", + "print('X shape', X.shape)\n", + "print('y shape', y.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "Ps6zo1g_zJlX", + "metadata": { + "id": "Ps6zo1g_zJlX" + }, + "source": [ + "Train & Test Data Split\n", + "Split the data for training and testing. Let's keep 25% for testing data size." + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "LcTtNROQzOof", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LcTtNROQzOof", + "outputId": "6c514f46-c1d7-49ff-d98f-2d0642a4560f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train: (4504, 104)\n", + "X_test: (1502, 104)\n", + "y_train: (4504,)\n", + "y_test: (1502,)\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n", + "\n", + "# Split the data into training and testing sets using 20% for testing\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n", + "\n", + "print('X_train: ', X_train.shape)\n", + "print('X_test: ', X_test.shape)\n", + "print('y_train: ', y_train.shape)\n", + "print('y_test: ', y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "iCAwj03c0l1m", + "metadata": { + "id": "iCAwj03c0l1m" + }, + "source": [ + "Just a note: I reran this initial baseline logistic regression model here until the prediction and metrics evaluation a few times with just the difference in the test size. I found that the accuracy score was better slightly at 25% instead of 20%. I also tried to vary with it the choice of scaler type which I explain later on below. The variance of the two makes a difference but again just slight. I'll continue with 25% for most of this notebook but will find that going up to 30% is sometimes better." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "GVMWo5HP0oX0", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GVMWo5HP0oX0", + "outputId": "4bdad4bf-bf7d-4da4-dc89-1c5928433055" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Null Values: 0\n" + ] + } + ], + "source": [ + "print(\"Total Null Values: \", X_train.isna().sum().sum())" + ] + }, + { + "cell_type": "markdown", + "id": "RPQ8ypso0zxr", + "metadata": { + "id": "RPQ8ypso0zxr" + }, + "source": [ + "#### Scale the Data.\n", + "\n", + "With logistic regression, always do a scaler. We'll use Robust Scaler." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "y8pLmP7T04Ee", + "metadata": { + "id": "y8pLmP7T04Ee" + }, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n", + "# Initialize the scaler\n", + "# scaler = StandardScaler()\n", + "# scaler = MinMaxScaler()\n", + "scaler = RobustScaler()\n", + "\n", + "# Standardize the features (mean=0 and variance=1)\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)" + ] + }, + { + "cell_type": "markdown", + "id": "DtU6KrU71Coz", + "metadata": { + "id": "DtU6KrU71Coz" + }, + "source": [ + "I tried processing this basline model in these next set of code cells with different types of Scalers: Standard, MinMax and Robust. With all the other parameters the same and the only difference being the scaler type, I found the Robust to be the highest scoreer (only slightly though) in accuracy for this model." + ] + }, + { + "cell_type": "markdown", + "id": "3lN9h3ce1Gz_", + "metadata": { + "id": "3lN9h3ce1Gz_" + }, + "source": [ + "Set up the required libraries." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "ZGWb2mhn1KCO", + "metadata": { + "id": "ZGWb2mhn1KCO" + }, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc, accuracy_score, roc_auc_score" + ] + }, + { + "cell_type": "markdown", + "id": "dxH2uo8R1Os7", + "metadata": { + "id": "dxH2uo8R1Os7" + }, + "source": [ + "Create the model, fit it to the training scaled data and predict the target variables." + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "hf2t_-px3Ebe", + "metadata": { + "id": "hf2t_-px3Ebe" + }, + "outputs": [], + "source": [ + "# Initialize and fit the logistic regression model\n", + "logregModel = LogisticRegression(random_state=42)\n", + "logregModel.fit(X_train_scaled, y_train)\n", + "# Predictions\n", + "y_pred = logregModel.predict(X_test_scaled)" + ] + }, + { + "cell_type": "markdown", + "id": "V-Dtp1_93KW8", + "metadata": { + "id": "V-Dtp1_93KW8" + }, + "source": [ + "Run some evaluation metrics on the prediction and trained model: accuracy score, classification, confusion, ROC and cross validation." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "7Iozo26U3MXQ", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 825 + }, + "id": "7Iozo26U3MXQ", + "outputId": "19d1aa33-6013-486f-be0f-3f6c6dab5a87" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 86.8842\n", + "Train Accuracy: 87.611\n", + "Test Accuracy: 86.8842\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.89 0.87 771\n", + " 1 0.88 0.85 0.86 731\n", + "\n", + " accuracy 0.87 1502\n", + " macro avg 0.87 0.87 0.87 1502\n", + "weighted avg 0.87 0.87 0.87 1502\n", + "\n", + "Cross-validation scores: [87.9 86.57 85.46 84.24 87.78]\n", + "Mean cross-validation score: 86.39\n", + "Precision scores: 88.142\n", + "Recall scores: 84.376\n", + "F1 scores: 86.218\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Evaluate the model\n", + "# Print the classification report and accuracy\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print(\"Accuracy:\", round(accuracy * 100, 4))\n", + "\n", + "# score the model accuracy scores and store in variables for display later\n", + "acc_train = logregModel.score(X_train_scaled, y_train)\n", + "acc_test = logregModel.score(X_test_scaled, y_test)\n", + "\n", + "# scores\n", + "print('Train Accuracy:', round(acc_train * 100, 4))\n", + "print('Test Accuracy:', round(acc_test * 100, 4))\n", + "\n", + "# Print classification report\n", + "print(\"\\nClassification Report:\")\n", + "print(classification_report(y_test, y_pred))\n", + "\n", + "#Cross Validation\n", + "# Perform 5-fold cross-validation\n", + "scores = cross_val_score(logregModel, X_train_scaled, y_train, cv=5, scoring='accuracy').round(4) * 100\n", + "print(\"Cross-validation scores:\", scores)\n", + "print(\"Mean cross-validation score:\", scores.mean().round(4))\n", + "precision_scores = cross_val_score(logregModel, X_train_scaled, y_train, cv=5, scoring='precision').round(4) * 100\n", + "print(\"Precision scores:\", precision_scores.mean().round(4))\n", + "recall_scores = cross_val_score(logregModel, X_train_scaled, y_train, cv=5, scoring='recall').round(4) * 100\n", + "print(\"Recall scores:\", recall_scores.mean().round(4))\n", + "f1_scores = cross_val_score(logregModel, X_train_scaled, y_train, cv=5, scoring='f1').round(4) * 100\n", + "print(\"F1 scores:\", f1_scores.mean().round(4))\n", + "\n", + "# Confusion matrix\n", + "conf_mat = confusion_matrix(y_test, y_pred)\n", + "plt.figure(figsize=(3, 2))\n", + "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')\n", + "plt.xlabel('Predicted')\n", + "plt.ylabel('Actual')\n", + "plt.title('Confusion Matrix')\n", + "plt.show()\n", + "\n", + "# ROC Curve\n", + "fpr, tpr, thresholds = roc_curve(y_test, logregModel.predict_proba(X_test_scaled)[:,1])\n", + "roc_auc = auc(fpr, tpr)\n", + "plt.figure(figsize=(3, 2))\n", + "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "9MaJG7Gh3euN", + "metadata": { + "id": "9MaJG7Gh3euN" + }, + "source": [ + "Baseline Logistic Regrssion Model with Robust scaled 25% test data set.\n", + "\n", + "Not bad with an accuracy of 86.88%. The Recall, F1 and Precision between 0 and 1 are pretty close too so it's predicting both values pretty well. An ROC curve area of .9293 is considered excellent (above .9) for overall performance of this classification model. The ideal place on this plot curve would be the least value for FPR (False Positive Rate) but highest TPR (True Positive Rate) so it would be right when the uptick of the plot starts to level off around .1." + ] + }, + { + "cell_type": "markdown", + "id": "HJ8tVu0x3iyw", + "metadata": { + "id": "HJ8tVu0x3iyw" + }, + "source": [ + "# PCA Dimension Reduction\n", + "Let's try to use PCA to reduce dimensionality.\n", + "\n", + "We'll start with 98% for the components to retain 98% of the variance of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "9r4weBGB3oRj", + "metadata": { + "id": "9r4weBGB3oRj" + }, + "outputs": [], + "source": [ + "from sklearn.decomposition import PCA\n", + "\n", + "# Here, we set n_components to 0.95 to retain 95% of the variance in the data\n", + "ncomponent = 0.98\n", + "pca = PCA(n_components=ncomponent)\n", + "\n", + "# Fit PCA on the scaled training data and transform both training and testing data\n", + "X_train_pca = pca.fit_transform(X_train_scaled)\n", + "X_test_pca = pca.transform(X_test_scaled)\n" + ] + }, + { + "cell_type": "markdown", + "id": "fzo37zQr3vL-", + "metadata": { + "id": "fzo37zQr3vL-" + }, + "source": [ + "Print out the shape of the data to confirm." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "GpGWPZAf3wCY", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GpGWPZAf3wCY", + "outputId": "2e91c8b4-481a-476c-aec1-44899654e2ee" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N Component: 0.98\n", + "X_train_scaled: (4504, 104)\n", + "X_test_scaled: (1502, 104)\n", + "X_train_pca: (4504, 63)\n", + "X_test_pca: (1502, 63)\n" + ] + } + ], + "source": [ + "# Confirm the X PCA shape has decreased\n", + "print('N Component:', ncomponent)\n", + "print('X_train_scaled: ', X_train_scaled.shape)\n", + "print('X_test_scaled: ', X_test_scaled.shape)\n", + "print('X_train_pca: ', X_train_pca.shape)\n", + "print('X_test_pca: ', X_test_pca.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "kPpORwCr3-t3", + "metadata": { + "id": "kPpORwCr3-t3" + }, + "source": [ + "With 98% in components the number of features comes down to 63 from 104. Something to note, the reduced number of features from PCA varies depending on the type of scaler used. Robust scaler tends to decrease features a lot more than Standard scaler even though the PCA component was the same.\n", + "\n", + "Let's run this through the logistic model: fit on training data, predict on test data and print out the evaluation metrics: accuracy score, classification, cross validation, and plot the confusion matrix and ROC graph." + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "8MvyyUcd4Fcv", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 825 + }, + "id": "8MvyyUcd4Fcv", + "outputId": "ff649282-ba4f-440c-c01f-4c1046cbc93f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 86.6178\n", + "Train Accuracy: 86.5897\n", + "Test Accuracy: 86.6178\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.85 0.89 0.87 771\n", + " 1 0.88 0.84 0.86 731\n", + "\n", + " accuracy 0.87 1502\n", + " macro avg 0.87 0.87 0.87 1502\n", + "weighted avg 0.87 0.87 0.87 1502\n", + "\n", + "Cross-validation scores: [86.79 86.35 85.35 84.91 87.22]\n", + "Mean cross-validation score: 86.124\n", + "Precision scores: 88.006\n", + "Recall scores: 83.936\n", + "F1 scores: 85.916\n" + ] + }, + { + "data": { + "image/png": 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XUAIjRE3QFJLIzdLUEMtnjYB3367QEQrwZ14hpi3Ziayc+wAAPR0tLP9iBIZ7dIeRoR7uPXyCjYmnEPvTWQCAdWsj3DyytM6+x87bgqTjWU12L1z1zz//IHrDOvzyyyEUFxXBxNQUviM+xOSp0+ucVi1dvAj7fvoR8xaEYtz4wKYPuJHQFJLIpaW+DlLj5yAt40/4fb4RhU+ew9bKBE+fV4jbRM4dCbfeHTFh4Xbce1gML5cu+CHUH/mPS3H41FU8eFSCdl6hEv1OHNkXcwIG4tffrr19SVKHrVti8dOeRCwLX4n2HTrgenY2Fn0VCn19fYz9NECibeqJ48i+8gdMzcyUFG3joSkkkUvIhIF4UFCCKYt3iMvy8p9ItHHuboMdh3/Hmcw/AQBxSb8haGRf9LK3xuFTV1FTw/Co+LnEOb4ePbA3ORPlFZWNfxMq4I8/LsN9gCf6u7kDANq0eQ9Hj/yCa9eyJdo9evQIEd8sRVTMFsycNkUJkTYuVZxCKnVM+eDBAyxcuBAeHh7o0qUL7O3t4eHhgYULF+L+/fvKDE0hfNy64dL1POyMnIh7JyKQvnsBJnwo+TWJc5fvYJhbN1iaGgIA+ve2g11bMxw/l1Nnn45drNCzsxW2HUhv9PhVhaOjEy6cP4+7d3MBADdv3EBWVib69XMTt6mpqcHC/81D4IQgdOhgp6xQG5WGhobUg4uUNgI7e/YshgwZAisrK3h7e8Pb2xuMMRQWFuLAgQNYt24djh49ir59+0rtRyQS1XpnEaupBq8ZvP/bpo0JPvu4H9buSEXklmT0dmiL7+ePgqjqH+w6fAEAELLyJ2xcNAa3k79BVVU1algNpi3dhXOX79TZZ4CfC3Lu5OP8H7lNeSucNnHSZygrew6/YUPA5/NRXV2NmbNmY4jPMHGbrVtiwdfUxJhx46X0xG00hVSg2bNnY9KkSVi9enW99cHBwcjIyJDaT0REBJYsWSJRxjd/H4LWfRQWa0NpaPBw6XoewtYfAgD8cfMB7Nu3xuSP+4kT2IxP3NGnWzuMnBWNvPwn+KBXB/wQOhoFRc9w8nfJL8xqCwUYPaQ3VsQea/J74bJjR4/gl8MHERH5PTp06IAbN3Lw7YoImJqawdfvQ1y/lo2dCduRuDdJJf+Rv0ZTSAXKzs7G1KlT662fMmUKsrOz661/LTQ0FKWlpRKHprmTIkNtsIKiZ8i5I/lKkRu5BbCyePUSN22hAEtmDseC75Nw5HQ2sv98iOgfT2Nv8iUEf+pZq78PvXpCV1sLO/9/8iOyWf19JCYGTcaQoT6w69gJw339MG58ALZs3gQAuJR5EU+eFGOwlwd6dbdHr+72ePjwb3z/7UoMGThAydErjoYGT+rBRUobgbVu3Rrnzp1Dp06d6qxPT09H69at39mPUCis9c6i5jB9BID0y3fQsa3k0yw7azPxQr5Akw8tgSZq3vo6anV1TZ3/hwr0c8UvaVdRVFLWeEGroJcVL2t9nnw+HzU1rz73Yb4j4OwiuTY5bXIQhg0fAb8PP2qyOBubKo4ulZbA5s6di6lTpyIzMxMDBw6Eubk5eDweCgoKkJKSgs2bN2PNmjXKCk8h1u1Ixcn4EMyb6I19KZfwftd2mDiyLz5fthsA8Lz8JU5f/BPhwX6oeFmFvPwn6OfUAWOH9cGCVUkSfdlameCDXu3hNzNKGbfCaW7uHoiNiYZFa0u079ABN3JykLBtK0Z8OBIA0LJlK7RsKflqY4GmACYmJmhnY6uMkBsFV0dZ0igtgU2fPh3GxsZYvXo1Nm3aJP4pJT6fDycnJ2zfvh3+/v7KCk8hMq/nYXRILJbO9MWXk4fg7t/FmPftPiQevShuM/5/cVg6cwTiwwPQykAXeflPsHjDYfFG1tcCRrjgYWEpjqffaOrb4Lz/LfwKG9b+gPBlS/DkSTFMzcww6uPRmDJthrJDa1J8vuolsGbxOp2qqioUFRUBAExMTCAQCP5Vf/Q6naZHr9NpevK+TqfrwmSp9de+8ZZa3xw1i42sAoFApvUuQkjD0RSSEMJZXN2sKg0lMELUhAo+hKQERoi6oCkkIYSzKIERQjiLNrISQjiLRmCEEM6iBEYI4SwVnEFSAiNEXdAIjBDCWbSRlRDCWTSFJIRwFk0hCSGcpbYJ7ODBgzJ36Ovr2+BgCCGNR0MF55AyJTA/Pz+ZOuPxeOIXExJCmhe1HYHV1NQ0dhyEkEbGV9cERgjhPhWcQTYsgZWXlyMtLQ15eXmorJT8efsvvvhCIYERQhSLr4IZTO4ElpWVhaFDh+LFixcoLy+HkZERioqKoKurCzMzM0pghDRTqrgGJvfW3NmzZ2P48OF48uQJdHR0cP78edy7dw9OTk747rvvGiNGQogCaPB4Ug8ukjuBXb58GSEhIeDz+eDz+RCJRLCyskJkZCS+/PLLxoiREKIAqvjL3HInMIFAIH4xmrm5OfLy8gAAhoaG4v9OCGl++Bo8qQcXyb0G5ujoiIsXL6Jjx47w8PDAokWLUFRUhISEBHTr1q0xYiSEKAA3U5R0co/AwsPDxb/huGzZMhgbG2PatGkoLCxETEyMwgMkhCiGKo7A5E5gvXv3hoeHBwDA1NQUR44cwbNnz3Dp0iX06NFD4QESQhSDx+NJPRoqIiICPB4PwcHB4jLGGBYvXgxLS0vo6OjA3d0d165dkzhPJBJh5syZMDExgZ6eHnx9ffHgwQO5rq16LwgihNSpMRbxMzIyEBMTg+7du0uUR0ZGYtWqVVi/fj0yMjJgYWGBgQMH4vnz5+I2wcHB2L9/PxITE3H27FmUlZVh2LBhcn0dUe41MBsbG6nZ+s6dO/J2SQhpAoqeJpaVlWHs2LGIjY3F8uXLxeWMMaxZswYLFy7ERx99BADYtm0bzM3NsWvXLkyZMgWlpaXYsmULEhIS4OXlBQDYsWMHrKyscPz4cQwaNEimGOROYG8OEwGgqqoKWVlZOHbsGObNmydvd4SQJvKu9CUSiSASiSTKhEIhhEJhne1nzJgBHx8feHl5SSSw3NxcFBQUwNvbW6IfNzc3nDt3DlOmTEFmZiaqqqok2lhaWsLBwQHnzp1rvAQ2a9asOss3bNiAixcvytsdIaSJvGsEFhERgSVLlkiUhYWFYfHixbXaJiYm4tKlS8jIyKhVV1BQAODVNqs3mZub4969e+I2WlpaaNWqVa02r8+XhcLWwIYMGYJ9+/YpqjtCiIK9aw0sNDQUpaWlEkdoaGitfu7fv49Zs2Zhx44d0NbWrvd6by81Mcbe+bBAljYS9yRzy3fYu3cvjIyMFNUdIUTB3vVVIqFQCAMDA4mjruljZmYmCgsL4eTkBE1NTWhqaiItLQ1r166FpqameOT19kiqsLBQXGdhYYHKykqUlJTU20YWDdrI+maGZIyhoKAAjx8/xsaNG+XtjhDSRBT1dSFPT09cvXpVomzChAno3LkzFixYAFtbW1hYWCAlJQWOjo4AgMrKSqSlpWHlypUAACcnJwgEAqSkpMDf3x8AkJ+fj+zsbERGRsoci9wJbMSIERIJTENDA6ampnB3d0fnzp3l7a5RPDi7RtkhqJ1Wo2gTc1OrODBZrvaKep2Ovr4+HBwcJMr09PRgbGwsLg8ODkZ4eDjs7OxgZ2eH8PBw6OrqYsyYMQBeffUwKCgIISEhMDY2hpGREebOnYtu3bqJn0rKQu4EVteCHiGk+WvKzfbz589HRUUFpk+fjpKSEjg7OyM5ORn6+vriNqtXr4ampib8/f1RUVEBT09PxMfHg8/ny3wdHmOMyRMYn89Hfn4+zMzMJMqLi4thZmbWLN6JX1z+j7JDUDvvjY1TdghqR94RWMihm1Lrvx/e6d+EoxRyj8Dqy3cikQhaWlr/OiBCSOPgq+D3bmROYGvXrgXw6tHo5s2b0aJFC3FddXU1Tp8+3WzWwAghtXH1pYXSyJzAVq9eDeDVCCw6OlpinqqlpYV27dohOjpa8RESQhSCr3r5S/YElpubCwDw8PBAUlJSrR20hJDmjauvzJFG7jWwkydPNkYchJBGpoL5S/6d+KNGjcKKFStqlX/77bf4+OOPFRIUIUTx6IWGANLS0uDj41OrfPDgwTh9+rRCgiKEKB6fx5N6cJHcU8iysrI6t0sIBAI8e/ZMIUERQhSPo4MsqeQegTk4OODHH3+sVZ6YmAh7e3uFBEUIUTxVnELKPQL7+uuvMXLkSNy+fRsDBgwAAJw4cQK7du3C3r17FR4gIUQx1Hoj62u+vr44cOAAwsPDsXfvXujo6KBHjx5ITU2FgYFBY8RICFEAtd7I+iYfHx/xQv7Tp0+xc+dOBAcH448//mgW34UkhNSmiiOwBt9Samoqxo0bB0tLS6xfvx5Dhw6lV0oT0oyp/VPIBw8eID4+HnFxcSgvL4e/vz+qqqqwb98+WsAnpJnj6Dq9VDKPwIYOHQp7e3tcv34d69atw8OHD7Fu3brGjI0QokBq/RQyOTkZX3zxBaZNmwY7O7vGjIkQ0gi4mqSkkXkEdubMGTx//hy9e/eGs7Mz1q9fj8ePHzdmbIQQBdJ4x8FFMsft4uKC2NhY5OfnY8qUKUhMTESbNm1QU1ODlJQUiZ8MJ4Q0P+/6VSIukjvx6urqYuLEiTh79iyuXr2KkJAQrFixAmZmZvD19W2MGAkhCqCKTyH/1cixU6dOiIyMxIMHD7B7925FxUQIaQQ8nvSDixq0kfVtfD4ffn5+8PPzU0R3hJBGwNVRljQKSWCEkOaPq+tc0lACI0RN8CiBEUK4iqaQhBDOUsF9rJTACFEXGlC9DEYJjBA1QYv4hBDOojUwQghnqWD+ogRGiLqgKSSRW1bmRezaHoebOddRVPQYEd+vhZuHp7j+1IkUHNi3BzdvXEfp06eI370XHTt1EdfnP/wbI4d519n38pWrMGDgoEa/B66xNNLF8vHO8O5lBR2hJv58+BTT1p9G1u0iAMCI/7RD0KAucGxvChMDbTjP3ocrucX19nfg68EY5GQN/4hfcej3e011GwpHU0git5cvK9ChYyf4+H6IL+cF16qvqKhA956OGDBwEFYsC6tVb2ZugUPJpyTKfk76CTu3xeE/fT9opKi5q6WeFlJXjEDa1YfwW3YUhaUVsLUwwNNykbiNrrYA6TmPkPTbHUR97ia1v5nDu4E1dtBNRAXzFyWwxubStx9c+vart37IsFdv8Mh/+Hed9Xw+H8YmphJlaSdPwNN7CHR19RQXqIoI+agnHhSVYcq6NHFZXmGZRJvdp/4EAFibtZDaV7d2RvhiRDd8MHc/7sZ/qvhgm5gqjsC4+h4ztXXj+jX8efMGhvt9pOxQmiWfPm1x6a8i7JznhXvxnyJ91UeYMLCz3P3oaPGxLcQTs2N+w6OnFY0QadOj94E1sfv372PixIlS24hEIjx79kziEIlEUs/hskM/70M7G1t06+Go7FCaJRtzfXw2uAv+yi+F75Ij2HwsB99PcsUYd/legx4Z5IrzNx7h8AXurnm9jfeOg4uadQJ78uQJtm3bJrVNREQEDA0NJY41361sogiblujlS6QcPYJhfiOVHUqzpcHj4fKdIoTtyMAfucXYkpyDrSk3MHmw7L+a5fN+W7h3s8S8LecaMdKmp4ovNFTqGtjBgwel1t+5c+edfYSGhmLOnDkSZWX/8P9VXM1V6vFkvHxZIV43I7UVlLxAzv2nEmU3HpTAz8VG5j7cu1vC1sIABTsDJcp3zx+I33IKMOirwwqItOnR2ygUzM/PDzweD4zV/5znXR+6UCiEUCiUKKsq/0ch8TU3h39OwgduHmjVykjZoTRb6TceoWMbQ4kyO8uWyHss+282fLfvMram3JAoy1z7MebHpeOXjDyFxKkMKpi/lDuFbN26Nfbt24eampo6j0uXLikzPIV48aIct27m4NbNHABA/t8PcOtmDgryHwIAnpU+xa2bOci9cxsAkHf3Lm7dzEFxkeQvPj3Iu4fLly7Cl6aPUq07eBV9Oppj3qiesLUwwOj+7THRuzM2HbkubtOqhRDdbYzRxaoVAKCjpSG62xjDvKUOAODR0wpczyuROADgflEZ7hVy98dr6JXSCubk5IRLly7V+yrqd43OuODG9Wv4fPIE8d9rV0UCAIYOH4GvloTjTNpJfLP4K3H9otC5AICJk6dj0tQZ4vLDP++HqZk5+rj0baLIuSnzr8cYvSIZSz/tgy/9e+Huo+eYtyUdiaf/Erfx6dMWsV+4i/9OmOcFAFiemIlvEjObOuQmw9UnjdLwmBIzxJkzZ1BeXo7BgwfXWV9eXo6LFy/CzU36ZsO3FavoFLI5e29snLJDUDsVBybL1f7S3WdS63u1M5Cpn4iICCQlJeHGjRvQ0dGBq6srVq5ciU6dOonbMMawZMkSxMTEoKSkBM7OztiwYQO6du0qbiMSiTB37lzs3r0bFRUV8PT0xMaNG/Hee+/JfE9KnUL269ev3uQFAHp6enInL0JI3Xg8ntRDVmlpaZgxYwbOnz+PlJQU/PPPP/D29kZ5ebm4TWRkJFatWoX169cjIyMDFhYWGDhwoMTvxwYHB2P//v1ITEzE2bNnUVZWhmHDhqG6ulr2e1LmCKyx0Ais6dEIrOnJOwL7I0/6+l0Pa/0GxfH48WOYmZkhLS0N/fv3B2MMlpaWCA4OxoIFCwC8Gm2Zm5tj5cqVmDJlCkpLS2FqaoqEhASMHj0aAPDw4UNYWVnhyJEjGDRItu/4Nut9YIQQBXrHTtaGbgovLS0FABgZvXo6npubi4KCAnh7/99LCIRCIdzc3HDu3Ku9dZmZmaiqqpJoY2lpCQcHB3EbWVACI0RNvOurRHVtCo+IiJDaJ2MMc+bMwQcffAAHBwcAQEFBAQDA3Nxcoq25ubm4rqCgAFpaWmjVqlW9bWRBX+YmRE28a5mrrk3hb++xfNvnn3+OK1eu4OzZs3VcT/KCjLF3rrXJ0uZNNAIjRE3w3vEfoVAIAwMDiUNaAps5cyYOHjyIkydPSjw5tLCwAIBaI6nCwkLxqMzCwgKVlZUoKSmpt40sKIERoiY0eNIPWTHG8PnnnyMpKQmpqamwsZH8mpaNjQ0sLCyQkpIiLqusrERaWhpcXV0BvNoDKhAIJNrk5+cjOztb3EYWNIUkRE0o6ruQM2bMwK5du/Dzzz9DX19fPNIyNDSEjo4OeDwegoODER4eDjs7O9jZ2SE8PBy6uroYM2aMuG1QUBBCQkJgbGwMIyMjzJ07F926dYOXl5fMsVACI0RNKGojflRUFADA3d1donzr1q0IDAwEAMyfPx8VFRWYPn26eCNrcnIy9PX/b6vG6tWroampCX9/f/FG1vj4ePD5sr+MgfaBEYWgfWBNT959YLcevZBa39Fc99+EoxQ0AiNETajidyEpgRGiJlQvfVECI0Rt0AsNCSGcJc9WCa6gBEaIuqAERgjhKlrEJ4RwFk0hCSEcpnoZjBIYIWqCRmCEEM6iNTBCCHepXv6iBEaIuqApJCGEs2gnPiGEs1QvfVECI0Rt0CI+IYSzVDB/UQIjRF1QAiOEcBZNIQkhnKV66YsSGCFqg7ZREEI4izayEkK4ixIYIYSrVHERXyV/F5KrRCIRIiIiEBoaCqFQqOxw1AJ95txGCawZefbsGQwNDVFaWgoDAwNlh6MW6DPnNg1lB0AIIQ1FCYwQwlmUwAghnEUJrBkRCoUICwujxeQmRJ85t9EiPiGEs2gERgjhLEpghBDOogRGCOEsSmCEEM6iBNZMbNy4ETY2NtDW1oaTkxPOnDmj7JBU2unTpzF8+HBYWlqCx+PhwIEDyg6JNAAlsGbgxx9/RHBwMBYuXIisrCz069cPQ4YMQV5enrJDU1nl5eXo0aMH1q9fr+xQyL9A2yiaAWdnZ/Tq1QtRUVHisi5dusDPzw8RERFKjEw98Hg87N+/H35+fsoOhciJRmBKVllZiczMTHh7e0uUe3t749y5c0qKihBuoASmZEVFRaiuroa5ublEubm5OQoKCpQUFSHcQAmsmXj7feWMMZV8hzkhikQJTMlMTEzA5/NrjbYKCwtrjcoIIZIogSmZlpYWnJyckJKSIlGekpICV1dXJUVFCDfQO/GbgTlz5uDTTz9F79694eLigpiYGOTl5WHq1KnKDk1llZWV4a+//hL/nZubi8uXL8PIyAjW1tZKjIzIg7ZRNBMbN25EZGQk8vPz4eDggNWrV6N///7KDktlnTp1Ch4eHrXKAwICEB8f3/QBkQahBEYI4SxaAyOEcBYlMEIIZ1ECI4RwFiUwQghnUQIjhHAWJTBCCGdRAiOEcBYlMEIIZ1ECI3JbvHgxevbsKf47MDBQKS8DvHv3Lng8Hi5fvtzk1ybNAyUwFRIYGAgejwcejweBQABbW1vMnTsX5eXljXrdH374Qeav31DSIYpEX+ZWMYMHD8bWrVtRVVWFM2fOYNKkSSgvL5d4XTUAVFVVQSAQKOSahoaGCumHEHnRCEzFCIVCWFhYwMrKCmPGjMHYsWNx4MAB8bQvLi4Otra2EAqFYIyhtLQUkydPhpmZGQwMDDBgwAD88ccfEn2uWLEC5ubm0NfXR1BQEF6+fClR//YUsqamBitXrkSHDh0gFAphbW2Nb775BgBgY2MDAHB0dASPx4O7u7v4vK1bt6JLly7Q1tZG586dsXHjRonrXLhwAY6OjtDW1kbv3r2RlZWlwE+OcBGNwFScjo4OqqqqAAB//fUX9uzZg3379oHP5wMAfHx8YGRkhCNHjsDQ0BCbNm2Cp6cnbt26BSMjI+zZswdhYWHYsGED+vXrh4SEBKxduxa2trb1XjM0NBSxsbFYvXo1PvjgA+Tn5+PGjRsAXiWhPn364Pjx4+jatSu0tLQAALGxsQgLC8P69evh6OiIrKwsfPbZZ9DT00NAQADKy8sxbNgwDBgwADt27EBubi5mzZrVyJ8eafYYURkBAQFsxIgR4r9///13ZmxszPz9/VlYWBgTCASssLBQXH/ixAlmYGDAXr58KdFP+/bt2aZNmxhjjLm4uLCpU6dK1Ds7O7MePXrUed1nz54xoVDIYmNj64wxNzeXAWBZWVkS5VZWVmzXrl0SZcuWLWMuLi6MMcY2bdrEjIyMWHl5ubg+Kiqqzr6I+qAppIo5fPgwWrRoAW1tbbi4uKB///5Yt24dAKBt27YwNTUVt83MzERZWRmMjY3RokUL8ZGbm4vbt28DAHJycuDi4iJxjbf/flNOTg5EIhE8PT1ljvnx48e4f/8+goKCJOJYvny5RBw9evSArq6uTHEQ9UBTSBXj4eGBqKgoCAQCWFpaSizU6+npSbStqalB69atcerUqVr9tGzZskHX19HRkfucmpoaAK+mkc7OzhJ1r6e6jF5bR+pACUzF6OnpoUOHDjK17dWrFwoKCqCpqYl27drV2aZLly44f/48xo8fLy47f/58vX3a2dlBR0cHJ06cwKRJk2rVv17zqq6uFpeZm5ujTZs2uHPnDsaOHVtnv/b29khISEBFRYU4SUqLg6gHmkKqMS8vL7i4uMDPzw+//vor7t69i3PnzuGrr77CxYsXAQCzZs1CXFwc4uLicOvWLYSFheHatWv19qmtrY0FCxZg/vz52L59O27fvo3z589jy5YtAAAzMzPo6Ojg2LFjePToEUpLSwG82hwbERGBH374Abdu3cLVq1exdetWrFq1CgAwZswYaGhoICgoCNevX8eRI0fw3XffNfInRJo9ZS/CEcV5exH/TWFhYRIL7689e/aMzZw5k1laWjKBQMCsrKzY2LFjWV5enrjNN998w0xMTFiLFi1YQEAAmz9/fr2L+IwxVl1dzZYvX87atm3LBAIBs7a2ZuHh4eL62NhYZmVlxTQ0NJibm5u4fOfOnaxnz55MS0uLtWrVivXv358lJSWJ69PT01mPHj2YlpYW69mzJ9u3bx8t4qs5eic+IYSzaApJCOEsSmCEEM6iBEYI4SxKYIQQzqIERgjhLEpghBDOogRGCOEsSmCEEM6iBEYI4SxKYIQQzqIERgjhrP8HlnmbsEGr/N0AAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Now apply the same logistic model to the reduced features\n", + "model = LogisticRegression()\n", + "model.fit(X_train_pca, y_train)\n", + "\n", + "# Predit\n", + "y_pred = model.predict(X_test_pca)\n", + "\n", + "# Evaluate the model\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print(\"Accuracy:\", round(accuracy * 100, 4))\n", + "\n", + "# score the model accuracy scores and store in variables for display later\n", + "acc_train_pca = model.score(X_train_pca, y_train)\n", + "acc_test_pca = model.score(X_test_pca, y_test)\n", + "\n", + "# scores\n", + "print('Train Accuracy:', round(acc_train_pca * 100, 4))\n", + "print('Test Accuracy:', round(acc_test_pca * 100, 4))\n", + "\n", + "# Print classification report\n", + "print(\"\\nClassification Report:\")\n", + "print(classification_report(y_test, y_pred))\n", + "\n", + "#Cross Validation\n", + "# Perform 5-fold cross-validation\n", + "scores = cross_val_score(model, X_train_pca, y_train, cv=5, scoring='accuracy').round(4) * 100\n", + "print(\"Cross-validation scores:\", scores)\n", + "print(\"Mean cross-validation score:\", scores.mean().round(4))\n", + "precision_scores = cross_val_score(model, X_train_pca, y_train, cv=5, scoring='precision').round(4) * 100\n", + "print(\"Precision scores:\", precision_scores.mean().round(4))\n", + "recall_scores = cross_val_score(model, X_train_pca, y_train, cv=5, scoring='recall').round(4) * 100\n", + "print(\"Recall scores:\", recall_scores.mean().round(4))\n", + "f1_scores = cross_val_score(model, X_train_pca, y_train, cv=5, scoring='f1').round(4) * 100\n", + "print(\"F1 scores:\", f1_scores.mean().round(4))\n", + "\n", + "# Confusion matrix\n", + "conf_mat = confusion_matrix(y_test, y_pred)\n", + "plt.figure(figsize=(3, 2))\n", + "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')\n", + "plt.xlabel('Predicted')\n", + "plt.ylabel('Actual')\n", + "plt.title('Confusion Matrix')\n", + "plt.show()\n", + "\n", + "\n", + "# ROC Curve\n", + "fpr, tpr, thresholds = roc_curve(y_test, model.predict_proba(X_test_pca)[:,1])\n", + "roc_auc = auc(fpr, tpr)\n", + "plt.figure(figsize=(3, 2))\n", + "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "EBY37GBy4kdJ", + "metadata": { + "id": "EBY37GBy4kdJ" + }, + "source": [ + "The accuracy improved slight up to 86.88 from 86.61.\n", + "\n", + "\n", + "\n", + "#### Model Evaluation Metrics Table\n", + "***Model Added: 2. Baseline with 98% PCA***\n", + "\n", + "| Number | Model | Accuracy | Training | Precision-0 | Precision-1 | Recall-0 | Recall-1 | F1-0 | F1-1 | Mean | Precision | Recall | F1 | ROC |\n", + "|--------|-------|----------|----------|-------------|-------------|-----------|-----------|-------|-------|-------|-----------|--------|------|-----|\n", + "| 1 | Baseline - Logistic Regression Model with Robust scaled 25% test data set | 86.8842 | 87.611 | 86 | 88 | 89 | 85 | 87 | 86 | 86.39 | 88.142 | 84.376 | 86.218 | 0.93|\n", + "| 2 | Baseline with 98% PCA | 86.6178 | 86.5897 | 85 | 88 | 89 | 84 | 87 | 87 | 86.124 | 88.006 | 83.936 | 85.916 | 0.92|\n", + "\n", + "\n", + "\n", + "Next let's try 95% PCA component.\n", + "\n", + "Let's run this through the logistic model: fit on training data, predict on test data and print out the evaluation metrics: accuracy score, classification, cross validation, and plot the confusion matrix and ROC graph." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "VA_NT73m4xAT", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 929 + }, + "id": "VA_NT73m4xAT", + "outputId": "ae4bffd7-c216-41f3-84b8-5d6ca095fd3a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N Component: 0.95\n", + "X_train_scaled: (4504, 104)\n", + "X_test_scaled: (1502, 104)\n", + "X_train_pca: (4504, 47)\n", + "X_test_pca: (1502, 47)\n", + "\n", + "Accuracy: 86.285\n", + "Train Accuracy: 86.2789\n", + "Test Accuracy: 86.285\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.85 0.89 0.87 771\n", + " 1 0.88 0.83 0.86 731\n", + "\n", + " accuracy 0.86 1502\n", + " macro avg 0.86 0.86 0.86 1502\n", + "weighted avg 0.86 0.86 0.86 1502\n", + "\n", + "Cross-validation scores: [86.13 86.35 83.68 83.91 86.44]\n", + "Mean cross-validation score: 85.302\n", + "Precision scores: 87.568\n", + "Recall scores: 82.616\n", + "F1 scores: 85.014\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Try n_components 0.95 to retain 95% of the variance in the data\n", + "ncomponent = 0.95\n", + "pca = PCA(n_components=ncomponent)\n", + "\n", + "# Fit PCA on the training data and transform both training and testing data\n", + "X_train_pca = pca.fit_transform(X_train_scaled)\n", + "X_test_pca = pca.transform(X_test_scaled)\n", + "\n", + "# Confirm the X PCA shape has decreased\n", + "print('N Component:', ncomponent)\n", + "print('X_train_scaled: ', X_train_scaled.shape)\n", + "print('X_test_scaled: ', X_test_scaled.shape)\n", + "print('X_train_pca: ', X_train_pca.shape)\n", + "print('X_test_pca: ', X_test_pca.shape)\n", + "\n", + "# Now apply the same logistic model to the reduced features\n", + "model = LogisticRegression()\n", + "model.fit(X_train_pca, y_train)\n", + "\n", + "# Predit\n", + "y_pred = model.predict(X_test_pca)\n", + "\n", + "# Evaluate the model\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print(\"\\nAccuracy:\", round(accuracy * 100, 4))\n", + "\n", + "# score the model accuracy scores and store in variables for display later\n", + "acc_train_pca = model.score(X_train_pca, y_train)\n", + "acc_test_pca = model.score(X_test_pca, y_test)\n", + "\n", + "# scores\n", + "print('Train Accuracy:', round(acc_train_pca * 100, 4))\n", + "print('Test Accuracy:', round(acc_test_pca * 100, 4))\n", + "\n", + "# Print classification report\n", + "print(\"\\nClassification Report:\")\n", + "print(classification_report(y_test, y_pred))\n", + "\n", + "#Cross Validation\n", + "# Perform 5-fold cross-validation\n", + "scores = cross_val_score(model, X_train_pca, y_train, cv=5, scoring='accuracy').round(4) * 100\n", + "print(\"Cross-validation scores:\", scores)\n", + "print(\"Mean cross-validation score:\", scores.mean().round(4))\n", + "precision_scores = cross_val_score(model, X_train_pca, y_train, cv=5, scoring='precision').round(4) * 100\n", + "print(\"Precision scores:\", precision_scores.mean().round(4))\n", + "recall_scores = cross_val_score(model, X_train_pca, y_train, cv=5, scoring='recall').round(4) * 100\n", + "print(\"Recall scores:\", recall_scores.mean().round(4))\n", + "f1_scores = cross_val_score(model, X_train_pca, y_train, cv=5, scoring='f1').round(4) * 100\n", + "print(\"F1 scores:\", f1_scores.mean().round(4))\n", + "\n", + "# Confusion matrix\n", + "conf_mat = confusion_matrix(y_test, y_pred)\n", + "plt.figure(figsize=(3, 2))\n", + "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')\n", + "plt.xlabel('Predicted')\n", + "plt.ylabel('Actual')\n", + "plt.title('Confusion Matrix')\n", + "plt.show()\n", + "\n", + "# ROC Curve\n", + "fpr, tpr, thresholds = roc_curve(y_test, model.predict_proba(X_test_pca)[:,1])\n", + "roc_auc = auc(fpr, tpr)\n", + "plt.figure(figsize=(3, 2))\n", + "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "NX_G4h4F5BiZ", + "metadata": { + "id": "NX_G4h4F5BiZ" + }, + "source": [ + "Going from 98% to 95% PCA component, the accuracy decreased from 86.6178% to 86.0693%.\n", + "\n", + "\n", + "#### Model Evaluation Metrics Table\n", + "***Model Added: 3. Baseline with 95% PCA***\n", + "\n", + "| Number | Model | Accuracy | Training | Precision-0 | Precision-1 | Recall-0 | Recall-1 | F1-0 | F1-1 | Mean | Precision | Recall | F1 | ROC |\n", + "|--------|-------|----------|----------|-------------|-------------|-----------|-----------|-------|-------|-------|-----------|--------|------|-----|\n", + "| 1 | Baseline - Logistic Regression Model with Robust scaled 25% test data set | 86.8842 | 87.611 | 86 | 88 | 89 | 85 | 87 | 86 | 86.39 | 88.142 | 84.376 | 86.218 | 0.93|\n", + "| 2 | Baseline with 98% PCA | 86.6178 | 86.5897 | 85 | 88 | 89 | 84 | 87 | 87 | 86.124 | 88.006 | 83.936 | 85.916 | 0.92|\n", + "| 3 | Baseline with 95% PCA | 86.0693 | 87.8553 | 87 | 84 | 93 | 72 | 90 | 77 | 87.37 | 87.692 | 70.002 | 77.83 | 0.92|\n", + "\n", + "\n", + "
\n", + "\n", + "Let's see if going further to 93% will continue to decrease accuracy. If so we'll end it here and settle with 98% as the winner.\n", + "\n", + "Same drill. Run this through the logistic model: fit on training data, predict on test data and print out the evaluation metrics: accuracy score, classification, cross validation, and plot the confusion matrix and ROC graph." + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "Xy06J5BL5FX6", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 929 + }, + "id": "Xy06J5BL5FX6", + "outputId": "26416b2d-3e86-4fa3-9db5-3930ded756c5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N Component: 0.93\n", + "X_train_scaled: (4504, 104)\n", + "X_test_scaled: (1502, 104)\n", + "X_train_pca: (4504, 39)\n", + "X_test_pca: (1502, 39)\n", + "\n", + "Accuracy: 85.8189\n", + "Train Accuracy: 85.857\n", + "Test Accuracy: 85.8189\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.85 0.88 0.86 771\n", + " 1 0.87 0.83 0.85 731\n", + "\n", + " accuracy 0.86 1502\n", + " macro avg 0.86 0.86 0.86 1502\n", + "weighted avg 0.86 0.86 0.86 1502\n", + "\n", + "Cross-validation scores: [86.35 85.46 83.91 84.24 86.22]\n", + "Mean cross-validation score: 85.236\n", + "Precision scores: 88.052\n", + "Recall scores: 81.868\n", + "F1 scores: 84.836\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Try n_components 0.93 to retain 93% of the variance in the data\n", + "ncomponent = 0.93\n", + "pca = PCA(n_components=ncomponent)\n", + "\n", + "# Fit PCA on the training data and transform both training and testing data\n", + "X_train_pca = pca.fit_transform(X_train_scaled)\n", + "X_test_pca = pca.transform(X_test_scaled)\n", + "\n", + "# Confirm the X PCA shape has decreased\n", + "print('N Component:', ncomponent)\n", + "print('X_train_scaled: ', X_train_scaled.shape)\n", + "print('X_test_scaled: ', X_test_scaled.shape)\n", + "print('X_train_pca: ', X_train_pca.shape)\n", + "print('X_test_pca: ', X_test_pca.shape)\n", + "\n", + "# Now apply the same logistic model to the reduced features\n", + "model = LogisticRegression()\n", + "model.fit(X_train_pca, y_train)\n", + "\n", + "# Predit\n", + "y_pred = model.predict(X_test_pca)\n", + "\n", + "# Evaluate the model\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print(\"\\nAccuracy:\", round(accuracy * 100, 4))\n", + "\n", + "# score the model accuracy scores and store in variables for display later\n", + "acc_train_pca = model.score(X_train_pca, y_train)\n", + "acc_test_pca = model.score(X_test_pca, y_test)\n", + "\n", + "# scores\n", + "print('Train Accuracy:', round(acc_train_pca * 100, 4))\n", + "print('Test Accuracy:', round(acc_test_pca * 100, 4))\n", + "\n", + "# Print classification report\n", + "print(\"\\nClassification Report:\")\n", + "print(classification_report(y_test, y_pred))\n", + "\n", + "#Cross Validation\n", + "# Perform 5-fold cross-validation\n", + "scores = cross_val_score(model, X_train_pca, y_train, cv=5, scoring='accuracy').round(4) * 100\n", + "print(\"Cross-validation scores:\", scores)\n", + "print(\"Mean cross-validation score:\", scores.mean().round(4))\n", + "precision_scores = cross_val_score(model, X_train_pca, y_train, cv=5, scoring='precision').round(4) * 100\n", + "print(\"Precision scores:\", precision_scores.mean().round(4))\n", + "recall_scores = cross_val_score(model, X_train_pca, y_train, cv=5, scoring='recall').round(4) * 100\n", + "print(\"Recall scores:\", recall_scores.mean().round(4))\n", + "f1_scores = cross_val_score(model, X_train_pca, y_train, cv=5, scoring='f1').round(4) * 100\n", + "print(\"F1 scores:\", f1_scores.mean().round(4))\n", + "\n", + "# Confusion matrix\n", + "conf_mat = confusion_matrix(y_test, y_pred)\n", + "plt.figure(figsize=(3, 2))\n", + "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')\n", + "plt.xlabel('Predicted')\n", + "plt.ylabel('Actual')\n", + "plt.title('Confusion Matrix')\n", + "plt.show()\n", + "\n", + "# ROC Curve\n", + "fpr, tpr, thresholds = roc_curve(y_test, model.predict_proba(X_test_pca)[:,1])\n", + "roc_auc = auc(fpr, tpr)\n", + "plt.figure(figsize=(3, 2))\n", + "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "uVEc4xtO5SY7", + "metadata": { + "id": "uVEc4xtO5SY7" + }, + "source": [ + "93% PCA is less than 95% as well.\n", + "\n", + "So far we find that applying PCA wasn't an improvement. The base initial logistic regression model using a Robust scaler and splitting test data size at 25% seems to be best scoring.\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "#### Model Evaluation Metrics Table\n", + "***Model Added: 4. Baseline with 93% PCA***\n", + "\n", + "| Number | Model | Accuracy | Training | Precision-0 | Precision-1 | Recall-0 | Recall-1 | F1-0 | F1-1 | Mean | Precision | Recall | F1 | ROC |\n", + "|--------|-------|----------|----------|-------------|-------------|-----------|-----------|-------|-------|-------|-----------|--------|------|-----|\n", + "| 1 | Baseline - Logistic Regression Model with Robust scaled 25% test data set | 86.8842 | 87.611 | 86 | 88 | 89 | 85 | 87 | 86 | 86.39 | 88.142 | 84.376 | 86.218 | 0.93|\n", + "| 2 | Baseline with 98% PCA | 86.6178 | 86.5897 | 85 | 88 | 89 | 84 | 87 | 87 | 86.124 | 88.006 | 83.936 | 85.916 | 0.92|\n", + "| 3 | Baseline with 95% PCA | 86.0693 | 87.8553 | 87 | 84 | 93 | 72 | 90 | 77 | 87.37 | 87.692 | 70.002 | 77.83 | 0.92|\n", + "| 4 | Baseline with 93% PCA | 85.8189 | 85.857 | 85 | 87 | 88 | 83 | 87 | 86 | 85.236 | 88.052 | 81.868 | 84.836 | 0.92 |\n" + ] + }, + { + "cell_type": "markdown", + "id": "4VRytygk5l_1", + "metadata": { + "id": "4VRytygk5l_1" + }, + "source": [ + "# Learning and Validation Curves\n", + "Learning and validation curves help diagnose the behavior of a model during training. These curves help us understanding how well a model generalizes to new data and are essential for tuning model parameters and avoiding problems like overfitting or underfitting.\n", + "\n", + "## Learning Curve with Logistic Regression\n", + "Let's use this on our highest scoring model which is the logistic regression with robust scaler and PCA." + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "CK6YuDqi5rEi", + "metadata": { + "id": "CK6YuDqi5rEi" + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import learning_curve\n", + "from sklearn.model_selection import ShuffleSplit" + ] + }, + { + "cell_type": "markdown", + "id": "hUgZhJfX5qcA", + "metadata": { + "id": "hUgZhJfX5qcA" + }, + "source": [ + "Let's plot a learning curve using the ShuffleSplit as a cross-validation strategy." + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "r9viJlmL7xSW", + "metadata": { + "id": "r9viJlmL7xSW" + }, + "outputs": [], + "source": [ + "\n", + "# Create cross-validation training and test scores for various training set sizes\n", + "train_sizes, train_scores, test_scores = learning_curve(LogisticRegression(),\n", + " X_train_pca,\n", + " y_train,\n", + " cv=ShuffleSplit(n_splits=5, test_size=0.2, random_state=0),\n", + " n_jobs=-1,\n", + " train_sizes=np.linspace(.1, 1.0, 5))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "UGLUwtRM745v", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 487 + }, + "id": "UGLUwtRM745v", + "outputId": "e737e80c-340a-46fa-c9c2-e224da658461" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Create means and standard deviations of training set scores\n", + "train_mean = np.mean(train_scores, axis=1)\n", + "train_std = np.std(train_scores, axis=1)\n", + "\n", + "# Create means and standard deviations of test set scores\n", + "test_mean = np.mean(test_scores, axis=1)\n", + "test_std = np.std(test_scores, axis=1)\n", + "\n", + "# Draw lines\n", + "plt.plot(train_sizes, train_mean, '--', color=\"#111111\", label=\"Training score\")\n", + "plt.plot(train_sizes, test_mean, color=\"#111111\", label=\"Cross-validation score\")\n", + "\n", + "# Draw bands\n", + "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color=\"#DDDDDD\")\n", + "plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, color=\"#DDDDDD\")\n", + "\n", + "# Create plot\n", + "plt.title(\"Learning Curve\")\n", + "plt.xlabel(\"Training Set Size\"), plt.ylabel(\"Accuracy Score\"), plt.legend(loc=\"best\")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "-T81VPkU74Is", + "metadata": { + "id": "-T81VPkU74Is" + }, + "source": [ + "Plot the learning curve below." + ] + }, + { + "cell_type": "markdown", + "id": "J403Y-S28Bul", + "metadata": { + "id": "J403Y-S28Bul" + }, + "source": [ + "This learning curve shows the progression of how well the model is working as the training data size increases. It shows that the training score started out well on the left side telling us that the model is good at fitting smaller sized data. But the decreasing training score as the data size increases indicates that it has a harder time with growing data probably due to the larger diversity of the data as it increases.\n", + "\n", + "The solid cross-validation shows the opposite effect. It starts with a low accuracy score with a small data because it has yet to learn on data it has yet to see. But the model is learning and generalizing better with more data. It tapers off at a certain point suggesting a diminishing return on accuracy from adding more data.\n", + "\n", + "The convergence of the two lines between the training score and cross-validation score tells us that adding more data is helping the model generalize better, but there may be a limit to the benefits of adding more training data.\n", + "\n", + "The existence of the generalization gap between the two lines on the right side might mean that the model might be overfitting to the training data. But the fact that it's converging is good sign at learning the data. The overfitting potential could be remedied by adding regularization or more feature engineering. The gap does seem to be closing in so the overfitting is not an issue." + ] + }, + { + "cell_type": "markdown", + "id": "mPQASOAy8Gtx", + "metadata": { + "id": "mPQASOAy8Gtx" + }, + "source": [ + "# Validation Curve for Logistic Regression\n", + "A validation curve is similar to learning curves in that it gives us an idea of how well the model is performing. But instead of the training data size variation it uses a variation of values for a specific hyperparameter. For example, C parameter in logistic regression, the n_estimators in a random forest or max_depth in a decision tree. It's used to find the best value for a parameter.\n", + "\n", + "Let's create a function to plot the validation curve so it may be reused later for the other models." + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "azKo7A1Z8QVD", + "metadata": { + "id": "azKo7A1Z8QVD" + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import validation_curve\n", + "\n", + "def plot_validation_curve(estimator, title, X, y, param_name, param_range,\n", + " cv=None, scoring=\"accuracy\", n_jobs=None):\n", + " \"\"\"\n", + " Plots a validation curve for a specified parameter of an estimator.\n", + "\n", + " :param estimator: The model (classifier or regressor).\n", + " :param title: Title for the chart.\n", + " :param X: Input features.\n", + " :param y: Target variable.\n", + " :param param_name: The name of the parameter to vary.\n", + " :param param_range: The range of values for the parameter.\n", + " :param cv: Number of cross-validation folds.\n", + " :param scoring: Scoring method to evaluate the predictions on the test set.\n", + " :param n_jobs: Number of jobs to run in parallel.\n", + " \"\"\"\n", + " train_scores, test_scores = validation_curve(\n", + " estimator, X, y, param_name=param_name, param_range=param_range,\n", + " cv=cv, scoring=scoring, n_jobs=n_jobs)\n", + "\n", + " train_scores_mean = np.mean(train_scores, axis=1)\n", + " train_scores_std = np.std(train_scores, axis=1)\n", + " test_scores_mean = np.mean(test_scores, axis=1)\n", + " test_scores_std = np.std(test_scores, axis=1)\n", + "\n", + " plt.figure(figsize=(8, 6))\n", + " plt.title(title)\n", + " plt.xlabel(param_name)\n", + " plt.ylabel(\"Score\")\n", + " plt.ylim(0.0, 1.1)\n", + " plt.semilogx(param_range, train_scores_mean, label=\"Training score\",\n", + " color=\"darkorange\", lw=2)\n", + " plt.fill_between(param_range, train_scores_mean - train_scores_std,\n", + " train_scores_mean + train_scores_std, color=\"darkorange\",\n", + " alpha=0.2)\n", + " plt.semilogx(param_range, test_scores_mean, label=\"Cross-validation score\",\n", + " color=\"navy\", lw=2)\n", + " plt.fill_between(param_range, test_scores_mean - test_scores_std,\n", + " test_scores_mean + test_scores_std, color=\"navy\", alpha=0.2)\n", + " plt.legend(loc=\"best\")\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "_cOzu28o8U43", + "metadata": { + "id": "_cOzu28o8U43" + }, + "source": [ + "Now pass in the logistic model to plot the validation curve." + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "UulDcnYM8Wis", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 569 + }, + "id": "UulDcnYM8Wis", + "outputId": "61a111a2-97ff-427c-ae7a-bc4d53307ba7" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Logistic Regression model\n", + "model = LogisticRegression(solver='liblinear') # solver specified for small datasets and binary classification\n", + "\n", + "# Parameter range (inverse of regularization strength; smaller values specify stronger regularization)\n", + "param_range = np.logspace(-3, 3, 10)\n", + "\n", + "# Plotting the validation curve\n", + "plot_validation_curve(model, \"Validation Curve for Logistic Regression: C\",\n", + " X_train_pca, y_train, param_name='C', param_range=param_range, cv=5, scoring=\"accuracy\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "rVFS_iAE8hMo", + "metadata": { + "id": "rVFS_iAE8hMo" + }, + "source": [ + " the 2 lines are pretty much right on point with each other. Since the training and cross-validation scores are close across the range of ๐ถ C values, the model generalizes well without significant overfitting or underfitting. This is evidenced by the lack of a large gap between the training and cross-validation lines." + ] + }, + { + "cell_type": "markdown", + "id": "bbHI8hp39GXP", + "metadata": { + "id": "bbHI8hp39GXP" + }, + "source": [ + "# K-Nearest Neighbors\n", + "Using previously scaled (Robust) test data with PCA, let's apply KNN model with 5 neighbors." + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "SDxO5PAz9V5c", + "metadata": { + "id": "SDxO5PAz9V5c" + }, + "outputs": [], + "source": [ + "from sklearn.neighbors import KNeighborsClassifier" + ] + }, + { + "cell_type": "markdown", + "id": "BPXGK11O9eSZ", + "metadata": { + "id": "BPXGK11O9eSZ" + }, + "source": [ + "In the block of code below, we will fit and predict a KNN model just like we did previously with Logistic Regression. Once we get a y data prediction we will evaluate the accuracy using Accuracy Score for both training and testing data, print a classification report, and confusion matrix.\n", + "\n", + "We used PCA data at 98% in this sample." + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "7uVl-fZW9f7t", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 842 + }, + "id": "7uVl-fZW9f7t", + "outputId": "8e2e8cf2-dab1-47ac-8eca-d2b656569b50" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KNN on PCA component data: 0.98\n", + "Accuracy: 79.9601\n", + "Train Accuracy: 87.944\n", + "Test Accuracy: 79.9601\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.78 0.80 771\n", + " 1 0.78 0.82 0.80 731\n", + "\n", + " accuracy 0.80 1502\n", + " macro avg 0.80 0.80 0.80 1502\n", + "weighted avg 0.80 0.80 0.80 1502\n", + "\n", + "Cross-validation scores: [80.13 78.47 79.69 80.24 78.33]\n", + "Mean cross-validation score: 79.372\n", + "Precision scores: 80.856\n", + "Recall scores: 77.462\n", + "F1 scores: 79.106\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ncomponent = 0.98\n", + "pca = PCA(n_components=ncomponent)\n", + "\n", + "# Fit PCA on the scaled training data and transform both training and testing data\n", + "X_train_pca = pca.fit_transform(X_train_scaled)\n", + "X_test_pca = pca.transform(X_test_scaled)\n", + "\n", + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "#fit the model on training data\n", + "knn.fit(X_train_pca, y_train)\n", + "# Predict on test data\n", + "y_pred = knn.predict(X_test_pca)\n", + "\n", + "# Evaluate the model\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print('KNN on PCA component data: ',ncomponent)\n", + "print(\"Accuracy:\", round(accuracy * 100, 4))\n", + "\n", + "# score the model accuracy scores and store in variables for display later\n", + "acc_train_knn = knn.score(X_train_pca, y_train)\n", + "acc_test_knn = knn.score(X_test_pca, y_test)\n", + "\n", + "# scores\n", + "print('Train Accuracy:', round(acc_train_knn * 100, 4))\n", + "print('Test Accuracy:', round(acc_test_knn * 100, 4))\n", + "\n", + "# Print classification report\n", + "print(\"\\nClassification Report:\")\n", + "print(classification_report(y_test, y_pred))\n", + "\n", + "#Cross Validation\n", + "# Perform 5-fold cross-validation\n", + "scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='accuracy').round(4) * 100\n", + "print(\"Cross-validation scores:\", scores)\n", + "print(\"Mean cross-validation score:\", scores.mean().round(4))\n", + "precision_scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='precision').round(4) * 100\n", + "print(\"Precision scores:\", precision_scores.mean().round(4))\n", + "recall_scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='recall').round(4) * 100\n", + "print(\"Recall scores:\", recall_scores.mean().round(4))\n", + "f1_scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='f1').round(4) * 100\n", + "print(\"F1 scores:\", f1_scores.mean().round(4))\n", + "\n", + "# Confusion matrix\n", + "conf_mat = confusion_matrix(y_test, y_pred)\n", + "plt.figure(figsize=(3, 2))\n", + "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')\n", + "plt.xlabel('Predicted')\n", + "plt.ylabel('Actual')\n", + "plt.title('Confusion Matrix')\n", + "plt.show()\n", + "\n", + "# ROC Curve\n", + "fpr, tpr, thresholds = roc_curve(y_test, knn.predict_proba(X_test_pca)[:,1])\n", + "roc_auc = auc(fpr, tpr)\n", + "plt.figure(figsize=(3, 2))\n", + "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3XIAIqJL9rVq", + "metadata": { + "id": "3XIAIqJL9rVq" + }, + "source": [ + "KNN with Robust scaler and PCA 98% gives an accuracy of 79.96.%.\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "#### Model Evaluation Metrics Table\n", + "***Model Added: 5. KNN k=5 with 98% PCA***\n", + "\n", + "| Number | Model | Accuracy | Training | Precision-0 | Precision-1 | Recall-0 | Recall-1 | F1-0 | F1-1 | Mean | Precision | Recall | F1 | ROC |\n", + "|--------|-------|----------|----------|-------------|-------------|-----------|-----------|-------|-------|-------|-----------|--------|------|-----|\n", + "| 1 | Baseline - Logistic Regression Model with Robust scaled 25% test data set | 86.8842 | 87.611 | 86 | 88 | 89 | 85 | 87 | 86 | 86.39 | 88.142 | 84.376 | 86.218 | 0.93|\n", + "| 2 | Baseline with 98% PCA | 86.6178 | 86.5897 | 85 | 88 | 89 | 84 | 87 | 87 | 86.124 | 88.006 | 83.936 | 85.916 | 0.92|\n", + "| 3 | Baseline with 95% PCA | 86.0693 | 87.8553 | 87 | 84 | 93 | 72 | 90 | 77 | 87.37 | 87.692 | 70.002 | 77.83 | 0.92|\n", + "| 4 | Baseline with 93% PCA | 85.8189 | 85.857 | 85 | 87 | 88 | 83 | 87 | 86 | 85.236 | 88.052 | 81.868 | 84.836 | 0.92 |\n", + "| 5 | KNN k=5 with 98% PCA | 79.9601 | 87.944 | 82 | 78 | 78 | 82 | 80 | 80 | 79.372 | 80.856 | 77.462 | 79.106 | 0.89\n", + "\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n", + "Let's try PCA 95% and 93%. Same process below as previous: fit, predict, evaluate with various metrics for accuracy score on testing and training data and print the classification report and confusion matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "id": "YBQvZuSa9x5v", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 842 + }, + "id": "YBQvZuSa9x5v", + "outputId": "05849b30-807e-4d05-e4b3-b5552ca84cb7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KNN on PCA component data: 0.95\n", + "Accuracy: 79.9601\n", + "Train Accuracy: 88.2327\n", + "Test Accuracy: 79.9601\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.81 0.79 0.80 771\n", + " 1 0.79 0.81 0.80 731\n", + "\n", + " accuracy 0.80 1502\n", + " macro avg 0.80 0.80 0.80 1502\n", + "weighted avg 0.80 0.80 0.80 1502\n", + "\n", + "Cross-validation scores: [79.69 78.36 79.36 80.13 79. ]\n", + "Mean cross-validation score: 79.308\n", + "Precision scores: 80.99\n", + "Recall scores: 77.066\n", + "F1 scores: 78.974\n" + ] + }, + { + "data": { + "image/png": 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Oh4cHNjY2BAUFkZiYqFeHVqtl/PjxuLi4YGdnR79+/bh48aLB1yQB7CHzqO3Iyg9f4eLumaTtn83BmLfwaeapV2baqF6c3f4RVw/M5oflE2jW0F0v3+sRF7785FWSd0Xyz96PWTdzOK5O9oiiHU04zKQJY+nTLZDHfZoTt/vHQmWSzp5h8oRxBHfsQJcn2hHyyotoUi4BkJFxjVkzPmRA/14E+rfl6Z5d+GTmR2TeuFHel2JUZmZmJR6GaNGiBSkpKbrj+PHjuryoqChmz57NwoULiY+Px93dnW7dunHjrt8vNDSU2NhYYmJi2LdvH5mZmfTp04e8vDyD2iFDyIeopr0Nu6InEhf/J/1fW0zq1Rs09HTh2o1sXZlJQ7vy+uDOjAxfx5/nU3nr1af4bul4WvV/n8ybWmytrfh28TiO//E3PUcuACB8bG82zhtFp1c+oQq+l/iBZWffpMmjTenT7z+ETZ5QKP/ihWRGDR9M3/7P8uqYcdSoYc+5pLNYqdUAXLl8mSuXLzP+jTfxatgITcolZn70HlcuXyZy1txyvhrjKW0IqdVq0Wq1emlqtRr1//8ud7OwsNDrdd2hKApz585l2rRpPPPMMwCsXr0aNzc31q9fz6hRo8jIyGDFihWsXbuWrl27ArBu3To8PT358ccf6dGjR5mvSXpgD9GkYd24qEln1PR1HE48T3LKVfYc+oOki1d0ZcYN7EzUih/4ZtevnDyTwoh31mJjbckLPdsB4N+mIfU9nHk1fB2Jf10i8a9LjAxfRzvvBgR1eLSiLq1SC3iyE6PHTaBzcLci85cunEfAk50YHzqZpo81p+4jnjzRMRAnJ2cAGjVuwoxP5tExsDOPeNajXYfHGf3aBPb9tJvbt2+X56UYVWlDyMjISBwdHfWOyMjIIuv6888/8fDwwMvLixdffJGzZ88CkJSUhEajoXv37rqyarWawMBA9u/fD0BCQgK5ubl6ZTw8PPD29taVKasKDWAXL15k2rRpdO7cmWbNmtG8eXM6d+7MtGnTuHDhQkU2zSh6B7bkyMlkPo8azvmdkRz4YirD/hOgy29Q15k6tR358cDvurSc3NvsTfiLx1s3BEBtZYGiKGhz/v2LcyvnNnl5+QS0aVR+F1NF5Ofns39fHPXqNWDC2Ffp2eVJhr/8QpHDzLtl3sjEzq4GFhamO2gpbQgZFhZGRkaG3hEWFlaoHj8/P9asWcMPP/zA8uXL0Wg0BAQEkJaWhkajAcDNzU3vO25ubro8jUaDlZUVtWrVKrZMma/JoNJGtG/fPpo1a0ZsbCytW7fmlVdeYfDgwbRu3Zqvv/6aFi1a8PPPP5daj1ar5fr163qHkm/YOPph8arrwqvPd+Sv5Mv0G7uIzzbs45MpzzGwTwcA3F0cAEi9qj+3kpp2AzfngrxDx8+RlZ3DRxOexsbaEltrKyJD+2Nubqb7vii79Ktp3Lx5kzWrPuPxgCeZt2Q5QZ278takCRw5HF/kdzKuXWPV8iX0f25AObfWuO6deL/3UKvVODg46B1FDR979uzJs88+S8uWLenatSvfffcdUDBUvPtcd1MUpdQhbFnK3KvC/jl54403GDFiBHPmzCk2PzQ0lPj4ov9Q3REZGcl7772nl2bu1h7LOh2M1tb7ZWam4sjJZMIXbgHg19MXad6oDiOf78j6bw/pyt07j6VS/Zt2JT2TQVNWMP/tFxj7UiD5+QpfbUvgyMlk8vLzy+9iqoj8/ILftVNQF14aPASAR5s247dfjxG74UvatmuvVz4rM5OJr4+mQcNGjBg5ttzba0wPayGrnZ0dLVu25M8//6R///5AQS+rTp06ujKpqam6Xpm7uzs5OTmkp6fr9cJSU1MJCAjAEBXWAztx4gSjR48uNn/UqFGcOHGi1HqK6vZauPkas6n3TXPlOqfO6neJf0/S4OleS5cP6Hpbd9R2stfrle08+Dst+r1HveAwHun8FiHvrMHDtSbn/057yFdQ9dSsVRNzCwsaNNQffjdo2BCNJkUvLSsri9BxI7GxsWXm7AVYWFqWZ1ONzsxMVeJxv7RaLadOnaJOnTp4eXnh7u7Ojh07dPk5OTnExcXpgpOvry+WlpZ6ZVJSUjhx4oTpBLA6deqUOGF34MABvQhenKK6vapK8vaVA8fO8mh9V720JvVcSU65CsC5v9NIuZxB8OOP6fItLczp6NuYg7+eLVRf2rUsMjKzCWz/KK5ONfg27nihMqJklpZWNG/uTfL5JL30C+fPUaeOh+5zVmYmE8aMwMLSkllzFxU5lDI1pQ0hy2ry5MnExcWRlJTEL7/8wnPPPcf169cZMmQIKpWK0NBQIiIiiI2N5cSJEwwdOhRbW1sGDhwIgKOjIyEhIUyaNImdO3dy9OhRBg8erBuSGqLChpCTJ09m9OjRJCQk0K1bN9zc3FCpVGg0Gnbs2MFnn33G3LlzK6p5RrFg3S52R0/izeHd2bjjCO1bNGD4s0/w2gdf6MosWr+bN0O681dyKn8lX2ZKSA+yb+Xy5feHdWVe7vc4p5M0XE7PxK+VF7PefI4Fn+/mz/OpFXFZld7Nm1lcvJCs+3zp77/54/QpHBwcca/jwaAhw/nv1Im0adsO33YdOLh/H/t+2sOi5dFAQc/r9bEjuHXrFtM/mklWViZZWZkA1KzlhLl55fgH0lAP0su628WLF3nppZe4cuUKtWvX5vHHH+fgwYPUr18fgClTppCdnc3YsWNJT0/Hz8+P7du3Y2//79rFOXPmYGFhwYABA8jOziY4OJjo6GiDf1uVUoELib788kvmzJlDQkKCbgGbubk5vr6+TJw4kQED7m/S1MbnNWM284H07OjN++P70bhebc79ncb8dbtYFavf85w2qhchzz5BLQdb4k+cIzTyK06e+Xc488Hr/Rjc93GcHG05f+kqn23Yx/x1u8r7Ukp06ed5Fd0EnYTDhxj36tBC6b369ufd9yMA2PL1RlavXM7l1H+oV78Br45+jU6dg0v8PsCm73bg4VH3YTXdILVsDfvL3vzt7SXmn4zoXmJ+ZVShAeyO3NxcrlwpWBvl4uKC5QPONVSmAFZdVKYAVl0YGsBaTCs5gCV+ZHoBrFIsarG0tCzTfJcQ4v4ZawhZmVSKACaEePhkOx0hhMmqgrvpSAATorqQIaQQwmRJABNCmKyquCOrBDAhqgnpgQkhTJYEMCGEyaqCI0gJYEJUF9IDE0KYLFnIKoQwWTKEFEKYLBlCCiFMVrUNYJs3by5zhf369bvvxgghHh6zKjiGLFMAu7NRf2lUKpXBb9YVQpSPatsDy5e33whh8syrawATQpi+KjiCvL8AlpWVRVxcHMnJyeTk5Ojlvf7660ZpmBDCuMyrYAQzOIAdPXqUXr16cfPmTbKysnBycuLKlSvY2tri6uoqAUyISqoqzoEZvDT3jTfeoG/fvly9ehUbGxsOHjzI+fPn8fX1ZdasWQ+jjUIIIzBTqUo87ldkZKTufZB3KIrC9OnT8fDwwMbGhqCgIBITE/W+p9VqGT9+PC4uLtjZ2dGvXz8uXrxo2DUZ2thjx44xadIkzM3NMTc3R6vV4unpSVRUFG+//bah1QkhysnDeDN3fHw8y5Yto1WrVnrpUVFRzJ49m4ULFxIfH4+7uzvdunXjxo1/3zgfGhpKbGwsMTEx7Nu3j8zMTPr06WPQSgaDA5ilpaVuYzQ3NzeSkwteIOro6Kj7byFE5WNupirxMFRmZiaDBg1i+fLl1KpVS5euKApz585l2rRpPPPMM3h7e7N69Wpu3rzJ+vXrAcjIyGDFihV88skndO3aFR8fH9atW8fx48f58ccfy9wGgwOYj48Phw8XvDW6c+fOvPvuu3z++eeEhobSsmVLQ6sTQpQTVSmHVqvl+vXreodWqy22vnHjxtG7d2+6du2ql56UlIRGo6F793/fM6lWqwkMDGT//oKXOickJJCbm6tXxsPDA29vb12ZsjA4gEVEROje4fjBBx/g7OzMmDFjSE1NZdmyZYZWJ4QoJ6X1wCIjI3F0dNQ7IiMji6wrJiaGI0eOFJmv0WiAghHa3dzc3HR5Go0GKysrvZ7bvWXKwuC7kO3atdP9d+3atdm6dauhVQghKkBpe+KHhYUxceJEvTS1Wl2o3IULF5gwYQLbt2/H2tq6zOdTFKXUNpSlzN2q3gZBQogilTaJr1arcXBw0DuKCmAJCQmkpqbi6+uLhYUFFhYWxMXFMX/+fCwsLHQ9r3t7Uqmpqbo8d3d3cnJySE9PL7ZMWRjcA/Py8ioxQp49e9bQKoUQ5cBYjxIFBwdz/PhxvbRhw4bx2GOPMXXqVBo2bIi7uzs7duzAx8cHgJycHOLi4pg5cyYAvr6+WFpasmPHDgYMGABASkoKJ06cICoqqsxtMTiA3b3WAyA3N5ejR4+ybds23nzzTUOrE0KUE2MtY7W3t8fb21svzc7ODmdnZ116aGgoERERNGnShCZNmhAREYGtrS0DBw4EClYthISEMGnSJJydnXFycmLy5Mm0bNmy0E2BkhgcwCZMmFBk+qJFi3R3J4UQlU95Psw9ZcoUsrOzGTt2LOnp6fj5+bF9+3bs7e11ZebMmYOFhQUDBgwgOzub4OBgoqOjMTc3L/N5VIqiKMZo8NmzZ2nTpg3Xr183RnUPxMbntYpuQrVz6ed5Fd2EaqeWbdn/ogOM2pBYYv6nz7V4kOZUCKPtRrFhwwacnJyMVZ0Qwsiq7YaGd/Px8dGbxFcUBY1Gw+XLl1m8eLFRGyeEMJ6q+DC3wQHs6aef1gtgZmZm1K5dm6CgIB577DGjNu5+pccvrOgmVDu1OslzsOUte3+EQeVlOx1g+vTpD6EZQoiHrQp2wAxfyGpubk5qamqh9LS0NIPuHgghypexH+auDAzugRV301Kr1WJlZfXADRJCPBzmVfC5mzIHsPnz5wMFzzd99tln1KhRQ5eXl5fHTz/9VGnmwIQQhVXru5Bz5swBCnpgS5cu1RsuWllZ0aBBA5YuXWr8FgohjMK86sWvsgewpKQkoGAPsE2bNhXaBkMIUbmZ6jxXSQyeA9u9e/fDaIcQ4iGrgvHL8LuQzz33HDNmzCiU/vHHH/P8888bpVFCCOOrinchDQ5gcXFx9O7du1D6U089xU8//WSURgkhjM9cpSrxMEUGDyEzMzOLXC5haWlZKR7kFkIUzUQ7WSUyuAfm7e3Nl19+WSg9JiaG5s2bG6VRQgjjq4pDSIN7YO+88w7PPvssZ86coUuXLgDs3LmT9evXs2HDBqM3UAhhHNV6Iesd/fr14+uvvyYiIoINGzZgY2ND69at2bVrFw4ODg+jjUIII6jWC1nv1rt3b91E/rVr13Tvhfz1118NequuEKL8VMUe2H1f0q5duxg8eDAeHh4sXLiQXr16yZbSQlRi1f4u5MWLF4mOjmblypVkZWUxYMAAcnNz2bhxo0zgC1HJmeg8fYnK3APr1asXzZs35+TJkyxYsIBLly6xYMGCh9k2IYQRVeu7kNu3b+f1119nzJgxNGnS5GG2SQjxEJhqkCpJmXtge/fu5caNG7Rr1w4/Pz8WLlzI5cuXH2bbhBBGZFbKYYrK3G5/f3+WL19OSkoKo0aNIiYmhrp165Kfn8+OHTu4cePGw2ynEOIBmalUJR5ltWTJElq1aoWDgwMODg74+/vz/fff6/IVRWH69Ol4eHhgY2NDUFAQiYn6r3TTarWMHz8eFxcX7Ozs6NevHxcvXjT8mgz9gq2tLcOHD2ffvn0cP36cSZMmMWPGDFxdXenXr5/BDRBClA9j3YV85JFHmDFjBocPH+bw4cN06dKFp59+WhekoqKimD17NgsXLiQ+Ph53d3e6deum18kJDQ0lNjaWmJgY9u3bR2ZmJn369DF4GZZRXmybl5fHli1bWLlyJZs3b37Q6h7YrdsV3YLqR95KVP4MfSvR+iMl93AGtn3kvtvi5OTExx9/zPDhw/Hw8CA0NJSpU6cCBb0tNzc3Zs6cyahRo8jIyKB27dqsXbuWF154AYBLly7h6enJ1q1b6dGjR5nPa5Shr7m5Of37968UwUsIUbTSemBarZbr16/rHVqttsQ68/LyiImJISsrC39/f5KSktBoNHTv3l1XRq1WExgYyP79+wFISEggNzdXr4yHhwfe3t66MmVlqnN3QggDlTYHFhkZiaOjo94RGRlZZF3Hjx+nRo0aqNVqRo8eTWxsLM2bN0ej0QDg5uamV97NzU2Xp9FosLKyKrSr891lyuq+HiUSQpgeVSnzXGFhYUycOFEvTa1WF1m2adOmHDt2jGvXrrFx40aGDBlCXFxcsedSFKXU85elzL0kgAlRTZQ2Ua9Wq4sNWPeysrKicePGALRr1474+HjmzZunm/fSaDTUqVNHVz41NVXXK3N3dycnJ4f09HS9XlhqaioBAQEGXZMMIYWoJsxUJR8PQlEUtFotXl5euLu7s2PHDl1eTk4OcXFxuuDk6+uLpaWlXpmUlBROnDhhcACTHpgQ1YQZxlmJ//bbb9OzZ088PT25ceMGMTEx7Nmzh23btqFSqQgNDSUiIoImTZrQpEkTIiIisLW1ZeDAgQA4OjoSEhLCpEmTcHZ2xsnJicmTJ9OyZUu6du1qUFskgAlRTRhrP7B//vmHl19+mZSUFBwdHWnVqhXbtm2jW7duAEyZMoXs7GzGjh1Leno6fn5+bN++HXt7e10dc+bMwcLCggEDBpCdnU1wcDDR0dF675stC6OsA6tsZB1Y+ZN1YOXP0HVg2xJLfvTvqRa1H6Q5FUJ6YEJUEya65VeJJIAJUU3IltLCYAmH44leuYJTJ09w+fJl5sxfRJfgfycqlyxawLbvv0Oj0WBpaUnz5i14bcIbtGrVWlfmQnIyn8yaybEjCeTk5PDEkx156+13cHZxqYhLqvSmhQTz35BgvTRN2g28+hYsynStVYMPx/aga4cmONpbs+/YOSbO3sKZi2kA1LK34Z0RXQnu0JhH3BxJu3aTLXtP8t6yHVzPKnllemVmqruulkQC2EOWnX2Tpk2b8vR/nmFS6PhC+fXrNyBs2rs88ognt7S3WLcmmjGvDmfL9ztwcnLi5s2bjB45nEebPsbylasBWLRgHuPHjWbdF19hZiYrYYqSePYfer++Qvc5L//fqd6vZg4m93Yez7+1lutZWl5/8Um2zh+Oz8C53LyVS53aDtRxsSds4fecOpdKPfeaLHizP3VcHBg4bX1FXI5RVMH4JQHsYXuyYyBPdgwsNr9Xn756nydPCSN24wb+/OM0fo/7c+zoES79/TdfbviaGjVqAPD+h5F0DOjAoV8O8ri/Yetmqovbt/P452pmofTGns74edej7aC5nEpKBWDCrG9I/m4aA7q1JnrLYU6e/YeX7gpUSX9fZfqn21kZPgBzczPy8vLL7TqMqSr2wOSf70okNyeHjf/7Ent7ex5t2hQoWASoUqn03oZupVZjZmbG0SMJFdXUSq+xpwtnv3mLUxsms+b9F2ngUbDiW21Z8G/2rZx/b1Xn5yvk5N4moFX9YutzqGHN9SytyQYvMN5+YJVJpQ5gFy5cYPjw4SWWuZ8n6CubuD27ebydD+3btmLtmmiWLl9JrVpOALRq3QYbGxvmfvIx2dnZ3Lx5k9mzosjPz5cdcYsRn3iBER/8j75vrGLsjFjcnGqw+9PRODnYcPr8Zc6npPPB6B7UtLfG0sKcyS93oo6LA+4u9kXW5+RgQ9iwzqz45lA5X4lxqUo5TFGlDmBXr15l9erVJZYp6gn6j2cW/QR9ZdW+gx9fbfyaNZ/H8MSTHXlzUihpaQUTyk5OTnw8ex5xcbvxb+/Dk4+3IzPzBs2at8Bc5r+KtP3gH3y9J5HEs/+w+/AZ/jO54M/Q4F5tuZ2Xz0tvf05jT2dSfniXq7um09GnIdv2nyYvr/CSSHtbNbGzhnAqKZWPVuws70sxqmr/WjVjK23/sLNnz5ZaR1FP0CvmZXsgtbKwtbWlXv361Ktfn1at29C3Z3e+3rSBkFdHARDwxJN8t+1H0tOvYm5ugYODA106PUHdnve/AV11cvNWLolnNDR6pOCu7dHTl3h86EIc7NRYWVpw5VoWPy0fQ8Lvf+t9r4atFZvnDCUzO4cXwj7ntgkPH6H03ShMUYUGsP79+6NSqSjpYYDSfvSinqA39ZX4iqKQk5NTKP3OsPKXgwe4ejWNoM5dyrtpJsnK0pzHGrjy86/n9dILlkRoafSIM20fq8t7y/99uNjeVs2WucPQ5tzmuSlr0eaY+B8q5C6k0dWpU4dFixbRv3//IvOPHTuGr69v+TbKyG5mZZGcnKz7/PfFi/x+6lTBcLdmTT5btpSgzl1wqV2bjGvX+DJmPf/8o6Fbj6d03/k6diMNGzaiVi0nfv31KFGREQx+ZSgNvBpWxCVVepGv9eS7fb9z4Z9ruNayY+rQztjbqfn8+yMAPNPZm8vXsrjwzzW8G7kzK7QPW346yc5DfwEFPa9v5w7DxtqSYe99hYOdGge7gn8kL1/LIj/fNJ++kwBmZL6+vhw5cqTYAFZa78wUJCaeYMSwV3SfZ0UVzM/1e/o//Df8PZKSzrL5m1iupadTs2ZNWni3ZNWaz2nc+N93b55LSmL+nNlkZGTgUbcuI0aO5uUhQ8v7UkxGXVdH1rz3As41bblyLYtDJy4Q+OpSkjXXAHB3sWfm671wdaqBJu0Gn39/lMhVu3Xf92lalw7e9QA4+b/JenU3fSZKV4+pMdU7jSWp0Ie59+7dS1ZWFk899VSR+VlZWRw+fJjAwOLXURXF1IeQpkge5i5/hj7MfeTc9RLz2zZweJDmVIgK7YF17NixxHw7OzuDg5cQomgyiS+EMFkPuutqZSQBTIjqQgKYEMJUVcVJfAlgQlQTVTB+SQATorpQVcExpAQwIaoJmcQXQpgsWUYhhDBZVTB+Ve7tdIQQxqNSlXyUVWRkJO3bt8fe3h5XV1f69+/P6dOn9cooisL06dPx8PDAxsaGoKAgEhMT9cpotVrGjx+Pi4sLdnZ29OvXj4sXLxp0TRLAhKgmjLUja1xcHOPGjePgwYPs2LGD27dv0717d7KysnRloqKimD17NgsXLiQ+Ph53d3e6devGjRs3dGVCQ0OJjY0lJiaGffv2kZmZSZ8+fcjLyytzW+TFtsIo5FnI8mfos5BnUrNLzG/kanNf7bh8+TKurq7ExcXRqVMnFEXBw8OD0NBQpk6dChT0ttzc3Jg5cyajRo0iIyOD2rVrs3btWl544QUALl26hKenJ1u3bqVHjx5lOrf0wISoJlQqVYnH/W7PnpGRARTsHgyQlJSERqOhe/fuujJqtZrAwED2798PQEJCArm5uXplPDw88Pb21pUpCwlgQlQTZqqSj6K2Z4+MLHl7dkVRmDhxIk8++STe3t4AaDQaANzc3PTKurm56fI0Gg1WVlbUqlWr2DJlIXchhaguSpnmKmp79nt3O77Xa6+9xm+//ca+ffsKn+6eeTVFUUpdylGWMneTHpgQ1URpk/hqtRoHBwe9o6QANn78eDZv3szu3bt55JF/38/g7u4OUKgnlZqaquuVubu7k5OTQ3p6erFlynRNZS4phDBppQ0hy0pRFF577TU2bdrErl278PLy0sv38vLC3d2dHTv+fcdATk4OcXFxBAQUvIjZ19cXS0tLvTIpKSmcOHFCV6YsZAgpRLVhnJWs48aNY/369XzzzTfY29vrelqOjo7Y2NigUqkIDQ0lIiKCJk2a0KRJEyIiIrC1tWXgwIG6siEhIUyaNAlnZ2ecnJyYPHkyLVu2pGvXrmVuiwQwIaoJYz0LuWTJEgCCgoL00letWsXQoUMBmDJlCtnZ2YwdO5b09HT8/PzYvn079vb/vjx4zpw5WFhYMGDAALKzswkODiY6Ohpzc/Myt0XWgQmjkHVg5c/QdWCajNwS890dLR+kORVCemBCVBdV8FlICWBCVBOynY4QwmTJdjpCCJNV9cKXBDAhqg15qYcQwmRVwfglAUyI6kICmBDCZMkQUghhsqpe+JIAJkS1IcsohBAmSxayCiFMlwQwIYSpqoqT+FVyNwpTpdVqiYyMJCwsrNStfIVxyG9u2iSAVSLXr1/H0dGRjIwMHBwcKro51YL85qZNtpQWQpgsCWBCCJMlAUwIYbIkgFUiarWa8PBwmUwuR/KbmzaZxBdCmCzpgQkhTJYEMCGEyZIAJoQwWRLAhBAmSwJYJbF48WK8vLywtrbG19eXvXv3VnSTqrSffvqJvn374uHhgUql4uuvv67oJon7IAGsEvjyyy8JDQ1l2rRpHD16lI4dO9KzZ0+Sk5MrumlVVlZWFq1bt2bhwoUV3RTxAGQZRSXg5+dH27ZtWbJkiS6tWbNm9O/fn8jIyApsWfWgUqmIjY2lf//+Fd0UYSDpgVWwnJwcEhIS6N69u1569+7d2b9/fwW1SgjTIAGsgl25coW8vDzc3Nz00t3c3NBoNBXUKiFMgwSwSuLe/coVRamSe5gLYUwSwCqYi4sL5ubmhXpbqamphXplQgh9EsAqmJWVFb6+vuzYsUMvfceOHQQEBFRQq4QwDbInfiUwceJEXn75Zdq1a4e/vz/Lli0jOTmZ0aNHV3TTqqzMzEz++usv3eekpCSOHTuGk5MT9erVq8CWCUPIMopKYvHixURFRZGSkoK3tzdz5syhU6dOFd2sKmvPnj107ty5UPqQIUOIjo4u/waJ+yIBTAhhsmQOTAhhsiSACSFMlgQwIYTJkgAmhDBZEsCEECZLApgQwmRJABNCmCwJYEIIkyUBTBhs+vTptGnTRvd56NChFbIZ4Llz51CpVBw7dqzczy0qBwlgVcjQoUNRqVSoVCosLS1p2LAhkydPJisr66Ged968eWV+/EaCjjAmeZi7innqqadYtWoVubm57N27lxEjRpCVlaW3XTVAbm4ulpaWRjmno6OjUeoRwlDSA6ti1Go17u7ueHp6MnDgQAYNGsTXX3+tG/atXLmShg0bolarURSFjIwMRo4ciaurKw4ODnTp0oVff/1Vr84ZM2bg5uaGvb09ISEh3Lp1Sy//3iFkfn4+M2fOpHHjxqjVaurVq8dHH30EgJeXFwA+Pj6oVCqCgoJ031u1ahXNmjXD2tqaxx57jMWLF+ud59ChQ/j4+GBtbU27du04evSoEX85YYqkB1bF2djYkJubC8Bff/3FV199xcaNGzE3Nwegd+/eODk5sXXrVhwdHfn0008JDg7mjz/+wMnJia+++orw8HAWLVpEx44dWbt2LfPnz6dhw4bFnjMsLIzly5czZ84cnnzySVJSUvj999+BgiDUoUMHfvzxR1q0aIGVlRUAy5cvJzw8nIULF+Lj48PRo0d59dVXsbOzY8iQIWRlZdGnTx+6dOnCunXrSEpKYsKECQ/51xOVniKqjCFDhihPP/207vMvv/yiODs7KwMGDFDCw8MVS0tLJTU1VZe/c+dOxcHBQbl165ZePY0aNVI+/fRTRVEUxd/fXxk9erRevp+fn9K6desiz3v9+nVFrVYry5cvL7KNSUlJCqAcPXpUL93T01NZv369XtoHH3yg+Pv7K4qiKJ9++qni5OSkZGVl6fKXLFlSZF2i+pAhZBXz7bffUqNGDaytrfH396dTp04sWLAAgPr161O7dm1d2YSEBDIzM3F2dqZGjRq6IykpiTNnzgBw6tQp/P399c5x7+e7nTp1Cq1WS3BwcJnbfPnyZS5cuEBISIheOz788EO9drRu3RpbW9sytUNUDzKErGI6d+7MkiVLsLS0xMPDQ2+i3s7OTq9sfn4+derUYc+ePYXqqVmz5n2d38bGxuDv5OfnAwXDSD8/P728O0NdRbatE0WQAFbF2NnZ0bhx4zKVbdu2LRqNBgsLCxo0aFBkmWbNmnHw4EFeeeUVXdrBgweLrbNJkybY2Niwc+dORowYUSj/zpxXXl6eLs3NzY26dety9uxZBg0aVGS9zZs3Z+3atWRnZ+uCZEntENWDDCGrsa5du+Lv70///v354YcfOHfuHPv37+e///0vhw8fBmDChAmsXLmSlStX8scffxAeHk5iYmKxdVpbWzN16lSmTJnCmjVrOHPmDAcPHmTFihUAuLq6YmNjw7Zt2/jnn3/IyMgAChbHRkZGMm/ePP744w+OHz/OqlWrmD17NgADBw7EzMyMkJAQTp48ydatW5k1a9ZD/oVEpVfRk3DCeO6dxL9beHi43sT7HdevX1fGjx+veHh4KJaWloqnp6cyaNAgJTk5WVfmo48+UlxcXJQaNWooQ4YMUaZMmVLsJL6iKEpeXp7y4YcfKvXr11csLS2VevXqKREREbr85cuXK56enoqZmZkSGBioS//888+VNm3aKFZWVkqtWrWUTp06KZs2bdLlHzhwQGndurViZWWltGnTRtm4caNM4ldzsie+EMJkyRBSCGGyJIAJIUyWBDAhhMmSACaEMFkSwIQQJksCmBDCZEkAE0KYLAlgQgiTJQFMCGGyJIAJIUyWBDAhhMn6P10kqm8NTRvHAAAAAElFTkSuQmCC", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ncomponent = 0.95\n", + "pca = PCA(n_components=ncomponent)\n", + "\n", + "# Fit PCA on the scaled training data and transform both training and testing data\n", + "X_train_pca = pca.fit_transform(X_train_scaled)\n", + "X_test_pca = pca.transform(X_test_scaled)\n", + "\n", + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "#fit the model on training data\n", + "knn.fit(X_train_pca, y_train)\n", + "# Predict on test data\n", + "y_pred = knn.predict(X_test_pca)\n", + "\n", + "# Evaluate the model\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print('KNN on PCA component data: ',ncomponent)\n", + "print(\"Accuracy:\", round(accuracy * 100, 4))\n", + "\n", + "# score the model accuracy scores and store in variables for display later\n", + "acc_train_knn = knn.score(X_train_pca, y_train)\n", + "acc_test_knn = knn.score(X_test_pca, y_test)\n", + "\n", + "# scores\n", + "print('Train Accuracy:', round(acc_train_knn * 100, 4))\n", + "print('Test Accuracy:', round(acc_test_knn * 100, 4))\n", + "\n", + "# Print classification report\n", + "print(\"\\nClassification Report:\")\n", + "print(classification_report(y_test, y_pred))\n", + "\n", + "#Cross Validation\n", + "# Perform 5-fold cross-validation\n", + "scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='accuracy').round(4) * 100\n", + "print(\"Cross-validation scores:\", scores)\n", + "print(\"Mean cross-validation score:\", scores.mean().round(4))\n", + "precision_scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='precision').round(4) * 100\n", + "print(\"Precision scores:\", precision_scores.mean().round(4))\n", + "recall_scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='recall').round(4) * 100\n", + "print(\"Recall scores:\", recall_scores.mean().round(4))\n", + "f1_scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='f1').round(4) * 100\n", + "print(\"F1 scores:\", f1_scores.mean().round(4))\n", + "\n", + "# Confusion matrix\n", + "conf_mat = confusion_matrix(y_test, y_pred)\n", + "plt.figure(figsize=(3, 2))\n", + "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')\n", + "plt.xlabel('Predicted')\n", + "plt.ylabel('Actual')\n", + "plt.title('Confusion Matrix')\n", + "plt.show()\n", + "\n", + "# ROC Curve\n", + "fpr, tpr, thresholds = roc_curve(y_test, knn.predict_proba(X_test_pca)[:,1])\n", + "roc_auc = auc(fpr, tpr)\n", + "plt.figure(figsize=(3, 2))\n", + "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d4R5dqek9-eg", + "metadata": { + "id": "d4R5dqek9-eg" + }, + "source": [ + "Not an improvement from 98% PCA.\n", + "\n", + "\n", + "\n", + "\n", + "#### Model Evaluation Metrics Table\n", + "***Model Added: 6. KNN k=5 with 95% PCA***\n", + "\n", + "| Number | Model | Accuracy | Training | Precision-0 | Precision-1 | Recall-0 | Recall-1 | F1-0 | F1-1 | Mean | Precision | Recall | F1 | ROC |\n", + "|--------|-------|----------|----------|-------------|-------------|-----------|-----------|-------|-------|-------|-----------|--------|------|-----|\n", + "| 1 | Baseline - Logistic Regression Model with Robust scaled 25% test data set | 86.8842 | 87.611 | 86 | 88 | 89 | 85 | 87 | 86 | 86.39 | 88.142 | 84.376 | 86.218 | 0.93|\n", + "| 2 | Baseline with 98% PCA | 86.6178 | 86.5897 | 85 | 88 | 89 | 84 | 87 | 87 | 86.124 | 88.006 | 83.936 | 85.916 | 0.92|\n", + "| 3 | Baseline with 95% PCA | 86.0693 | 87.8553 | 87 | 84 | 93 | 72 | 90 | 77 | 87.37 | 87.692 | 70.002 | 77.83 | 0.92|\n", + "| 4 | Baseline with 93% PCA | 85.8189 | 85.857 | 85 | 87 | 88 | 83 | 87 | 86 | 85.236 | 88.052 | 81.868 | 84.836 | 0.92 |\n", + "| 5 | KNN k=5 with 98% PCA | 79.9601 | 87.944 | 82 | 78 | 78 | 82 | 80 | 80 | 79.372 | 80.856 | 77.462 | 79.106 | 0.89|\n", + "| 6 | KNN k=5 with 95% PCA | 79.9601 | 88.2327 | 81 | 79 | 79 | 81 | 80 | 80 | 79.308 | 80.99 | 77.066 | 78.974 | 0.89|\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Now try 93%.\n", + "\n", + "Same process below as previous: fit, predict, evaluate with various metrics for accuracy score on testing and training data and print the classification report and confusion matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "vFB23tmz-DYH", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 842 + }, + "id": "vFB23tmz-DYH", + "outputId": "0c3a4f39-e17d-4b05-8ccb-f91ffdfe5304" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KNN on PCA component data: 0.93\n", + "Accuracy: 79.8269\n", + "Train Accuracy: 87.9218\n", + "Test Accuracy: 79.8269\n", + "\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.78 0.80 771\n", + " 1 0.78 0.82 0.80 731\n", + "\n", + " accuracy 0.80 1502\n", + " macro avg 0.80 0.80 0.80 1502\n", + "weighted avg 0.80 0.80 0.80 1502\n", + "\n", + "Cross-validation scores: [81.02 78.25 79.02 79.91 77.89]\n", + "Mean cross-validation score: 79.218\n", + "Precision scores: 80.88\n", + "Recall scores: 77.022\n", + "F1 scores: 78.896\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ncomponent = 0.93\n", + "pca = PCA(n_components=ncomponent)\n", + "\n", + "# Fit PCA on the scaled training data and transform both training and testing data\n", + "X_train_pca = pca.fit_transform(X_train_scaled)\n", + "X_test_pca = pca.transform(X_test_scaled)\n", + "\n", + "knn = KNeighborsClassifier(n_neighbors=5)\n", + "#fit the model on training data\n", + "knn.fit(X_train_pca, y_train)\n", + "# Predict on test data\n", + "y_pred = knn.predict(X_test_pca)\n", + "\n", + "# Evaluate the model\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print('KNN on PCA component data: ',ncomponent)\n", + "print(\"Accuracy:\", round(accuracy * 100, 4))\n", + "\n", + "# score the model accuracy scores and store in variables for display later\n", + "acc_train_knn = knn.score(X_train_pca, y_train)\n", + "acc_test_knn = knn.score(X_test_pca, y_test)\n", + "\n", + "# scores\n", + "print('Train Accuracy:', round(acc_train_knn * 100, 4))\n", + "print('Test Accuracy:', round(acc_test_knn * 100, 4))\n", + "\n", + "# Print classification report\n", + "print(\"\\nClassification Report:\")\n", + "print(classification_report(y_test, y_pred))\n", + "\n", + "#Cross Validation\n", + "# Perform 5-fold cross-validation\n", + "scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='accuracy').round(4) * 100\n", + "print(\"Cross-validation scores:\", scores)\n", + "print(\"Mean cross-validation score:\", scores.mean().round(4))\n", + "precision_scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='precision').round(4) * 100\n", + "print(\"Precision scores:\", precision_scores.mean().round(4))\n", + "recall_scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='recall').round(4) * 100\n", + "print(\"Recall scores:\", recall_scores.mean().round(4))\n", + "f1_scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='f1').round(4) * 100\n", + "print(\"F1 scores:\", f1_scores.mean().round(4))\n", + "\n", + "# Confusion matrix\n", + "conf_mat = confusion_matrix(y_test, y_pred)\n", + "plt.figure(figsize=(3, 2))\n", + "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')\n", + "plt.xlabel('Predicted')\n", + "plt.ylabel('Actual')\n", + "plt.title('Confusion Matrix')\n", + "plt.show()\n", + "\n", + "# ROC Curve\n", + "fpr, tpr, thresholds = roc_curve(y_test, knn.predict_proba(X_test_pca)[:,1])\n", + "roc_auc = auc(fpr, tpr)\n", + "plt.figure(figsize=(3, 2))\n", + "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "KL3U803W-MnE", + "metadata": { + "id": "KL3U803W-MnE" + }, + "source": [ + "Decreasing the PCA component did not show any improvement. So far the best scoring one is the baseline model Logistic Regression with Robust scaling at 25% test data.\n", + "\n", + "#### Model Evaluation Metrics Table\n", + "***Model Added: 7. KNN k=5 with 93% PCA***\n", + "\n", + "| Number | Model | Accuracy | Training | Precision-0 | Precision-1 | Recall-0 | Recall-1 | F1-0 | F1-1 | Mean | Precision | Recall | F1 | ROC |\n", + "|--------|-------|----------|----------|-------------|-------------|-----------|-----------|-------|-------|-------|-----------|--------|------|-----|\n", + "| 1 | Baseline - Logistic Regression Model with Robust scaled 25% test data set | 86.8842 | 87.611 | 86 | 88 | 89 | 85 | 87 | 86 | 86.39 | 88.142 | 84.376 | 86.218 | 0.93|\n", + "| 2 | Baseline with 98% PCA | 86.6178 | 86.5897 | 85 | 88 | 89 | 84 | 87 | 87 | 86.124 | 88.006 | 83.936 | 85.916 | 0.92|\n", + "| 3 | Baseline with 95% PCA | 86.0693 | 87.8553 | 87 | 84 | 93 | 72 | 90 | 77 | 87.37 | 87.692 | 70.002 | 77.83 | 0.92|\n", + "| 4 | Baseline with 93% PCA | 85.8189 | 85.857 | 85 | 87 | 88 | 83 | 87 | 86 | 85.236 | 88.052 | 81.868 | 84.836 | 0.92 |\n", + "| 5 | KNN k=5 with 98% PCA k=5 | 79.9601 | 87.944 | 82 | 78 | 78 | 82 | 80 | 80 | 79.372 | 80.856 | 77.462 | 79.106 | 0.89|\n", + "| 6 | KNN k=5 with 95% PCA k=5 | 79.9601 | 88.2327 | 81 | 79 | 79 | 81 | 80 | 80 | 79.308 | 80.99 | 77.066 | 78.974 | 0.89|\n", + "| 7 | KNN k=5 with 93% PCA k=5 | 79.8269 | 87.9218 | 82 | 78 | 78 | 82 | 80 | 80 | 79.218 | 80.88 | 77.022 | 78.896 | 0.88|\n" + ] + }, + { + "cell_type": "markdown", + "id": "wyg1WWt6-TFL", + "metadata": { + "id": "wyg1WWt6-TFL" + }, + "source": [ + "Let's try to find the right K neighbors value. \n", + "\n", + "First we will reset the training and test data for both the scaled and PCA variables to avoid any changes made.\n", + "Then we'll loop from 1-40 for k neighbors value and find the smallest mean error for the ideal k value. We'll do it twice to run against PCA data and then just scaled data.\n", + "\n", + "Since from the previous scores for KNN, .98 PCA gave the best score so we'll use .98 below." + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "WnMeGQim-ZoI", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "WnMeGQim-ZoI", + "outputId": "216a240b-6229-48ae-ba91-62cf6fb099ca" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Split the data into training and testing sets using 20% for testing\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n", + "\n", + "scaler = RobustScaler()\n", + "\n", + "# Standardize the features (mean=0 and variance=1)\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "ncomponent = 0.98\n", + "pca = PCA(n_components=ncomponent)\n", + "\n", + "# Fit PCA on the training data and transform both training and testing data\n", + "X_train_pca = pca.fit_transform(X_train_scaled)\n", + "X_test_pca = pca.transform(X_test_scaled)\n", + "\n", + "\n", + "errors = []\n", + "for i in range(1, 40):\n", + " knn = KNeighborsClassifier(n_neighbors=i)\n", + " knn.fit(X_train_scaled, y_train)\n", + " pred_i = knn.predict(X_test_scaled)\n", + " errors.append(np.mean(pred_i != y_test))\n", + "\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(range(1, 40), errors, color='red', linestyle='dashed', marker='o',\n", + " markerfacecolor='blue', markersize=10)\n", + "plt.title('Error Rate K Value')\n", + "plt.xlabel('K Value')\n", + "plt.ylabel('Mean Error')\n", + "plt.show()\n", + "\n", + "\n", + "errors = []\n", + "for i in range(1, 40):\n", + " knn = KNeighborsClassifier(n_neighbors=i)\n", + " knn.fit(X_train_pca, y_train)\n", + " pred_i = knn.predict(X_test_pca)\n", + " errors.append(np.mean(pred_i != y_test))\n", + "\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(range(1, 40), errors, color='red', linestyle='dashed', marker='o',\n", + " markerfacecolor='blue', markersize=10)\n", + "plt.title('Error Rate K Value')\n", + "plt.xlabel('K Value')\n", + "plt.ylabel('Mean Error')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "HRk6Me4f-mnH", + "metadata": { + "id": "HRk6Me4f-mnH" + }, + "source": [ + "Looks like having the k value of 51 has the lowest error for both training data (Robust scaled and PCA). Let's use a pipeline to accomplish all 3 steps, scaling, applying PCA (98%) and then KNN (k neighbors = 5). Then evaluate using Accuracy score and classification report. **\n", + "\n", + "I will run the pipeline code below with k=1 and .93 PCA." + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "ayuROQdN-oET", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 551 + }, + "id": "ayuROQdN-oET", + "outputId": "811ceacf-7cfa-4afa-e951-f16e89f247d8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 89.281\n", + "Train Accuracy: 100.0\n", + "Test Accuracy: 89.281\n", + "KNN Classification report: precision recall f1-score support\n", + "\n", + " 0 0.93 0.85 0.89 771\n", + " 1 0.86 0.94 0.89 731\n", + "\n", + " accuracy 0.89 1502\n", + " macro avg 0.90 0.89 0.89 1502\n", + "weighted avg 0.90 0.89 0.89 1502\n", + "\n", + "Cross-validation scores: [79.58 78.47 75.58 78.47 77.33]\n", + "Mean cross-validation score: 77.886\n", + "Precision scores: 84.702\n", + "Recall scores: 68.616\n", + "F1 scores: 75.798\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.pipeline import Pipeline\n", + "# Create a pipeline that does PCA first then applies KNN\n", + "pipeline = Pipeline([\n", + " ('scaler', RobustScaler()),\n", + " ('pca', PCA(n_components=.98)),\n", + " ('knn', KNeighborsClassifier(n_neighbors=1))\n", + "])\n", + "\n", + "pipeline.fit(X_train, y_train)\n", + "\n", + "# Predicting\n", + "y_pred = pipeline.predict(X_test)\n", + "\n", + "# score the model accuracy scores and store in variables for display later\n", + "knn_pca_pipl_train = pipeline.score(X_train, y_train)\n", + "knn_pca_pipl_test = pipeline.score(X_test, y_test)\n", + "\n", + "# Evaluation\n", + "print('Accuracy: ', round(accuracy_score(y_test, y_pred) * 100, 4))\n", + "# scores\n", + "print('Train Accuracy:', round(knn_pca_pipl_train * 100, 4))\n", + "print('Test Accuracy:', round(knn_pca_pipl_test * 100, 4))\n", + "print('KNN Classification report: ', classification_report(y_test, y_pred))\n", + "\n", + "#Cross Validation\n", + "# Perform 5-fold cross-validation\n", + "scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='accuracy').round(4) * 100\n", + "print(\"Cross-validation scores:\", scores)\n", + "print(\"Mean cross-validation score:\", scores.mean().round(4))\n", + "precision_scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='precision').round(4) * 100\n", + "print(\"Precision scores:\", precision_scores.mean().round(4))\n", + "recall_scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='recall').round(4) * 100\n", + "print(\"Recall scores:\", recall_scores.mean().round(4))\n", + "f1_scores = cross_val_score(knn, X_train_pca, y_train, cv=5, scoring='f1').round(4) * 100\n", + "print(\"F1 scores:\", f1_scores.mean().round(4))\n", + "\n", + "# ROC Curve\n", + "fpr, tpr, thresholds = roc_curve(y_test, knn.predict_proba(X_test_pca)[:,1])\n", + "roc_auc = auc(fpr, tpr)\n", + "plt.figure(figsize=(3, 2))\n", + "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "R7eSbxGh-4KC", + "metadata": { + "id": "R7eSbxGh-4KC" + }, + "source": [ + "this is a new high scoring record. Since identifying the best k value being 1 and plotting that out, we are getting a better result with 89.281 accuracy. Even better than our highest score with the baseline. The ROC curve area is not excellent (above .9) but good at .87.\n", + "\n", + "\n", + "\n", + "#### Model Evaluation Metrics Table\n", + "***Model Added: 8. KNN k=1 with 98% PCA***\n", + "\n", + "| Number | Model | Accuracy | Training | Precision-0 | Precision-1 | Recall-0 | Recall-1 | F1-0 | F1-1 | Mean | Precision | Recall | F1 | ROC |\n", + "|--------|-------|----------|----------|-------------|-------------|-----------|-----------|-------|-------|-------|-----------|--------|------|-----|\n", + "| 1 | Baseline - Logistic Regression Model with Robust scaled 25% test data set | 86.8842 | 87.611 | 86 | 88 | 89 | 85 | 87 | 86 | 86.39 | 88.142 | 84.376 | 86.218 | 0.93|\n", + "| 2 | Baseline with 98% PCA | 86.6178 | 86.5897 | 85 | 88 | 89 | 84 | 87 | 87 | 86.124 | 88.006 | 83.936 | 85.916 | 0.92|\n", + "| 3 | Baseline with 95% PCA | 86.0693 | 87.8553 | 87 | 84 | 93 | 72 | 90 | 77 | 87.37 | 87.692 | 70.002 | 77.83 | 0.92|\n", + "| 4 | Baseline with 93% PCA | 85.8189 | 85.857 | 85 | 87 | 88 | 83 | 87 | 86 | 85.236 | 88.052 | 81.868 | 84.836 | 0.92 |\n", + "| 5 | KNN k=5 with 98% PCA k=5 | 79.9601 | 87.944 | 82 | 78 | 78 | 82 | 80 | 80 | 79.372 | 80.856 | 77.462 | 79.106 | 0.89|\n", + "| 6 | KNN k=5 with 95% PCA k=5 | 79.9601 | 88.2327 | 81 | 79 | 79 | 81 | 80 | 80 | 79.308 | 80.99 | 77.066 | 78.974 | 0.89|\n", + "| 7 | KNN k=5 with 93% PCA k=5 | 79.8269 | 87.9218 | 82 | 78 | 78 | 82 | 80 | 80 | 79.218 | 80.88 | 77.022 | 78.896 | 0.88|\n", + "| 8 | KNN k=1 with 98% PCA k=5 | 89.281 | 100.0 | 93 | 86 | 85 | 94 | 89 | 89 | 77.886 | 84.702 | 68.616 | 75.798 | 0.87 |\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "Now let's plot a learning and validation curve for KNN.\n", + "\n", + "#### Learning Curve for KNN\n", + "\n", + "This time we will create a function to plot the learning curve like we did with the previous validation curve plot for Logistic Regression. We'll be using this again later for the other models as well. \n", + "\n", + "Define the learning curve plot function below." + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "Z_cWGRxN--uh", + "metadata": { + "id": "Z_cWGRxN--uh" + }, + "outputs": [], + "source": [ + "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n", + " n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n", + " plt.figure()\n", + " plt.title(title)\n", + " if ylim is not None:\n", + " plt.ylim(*ylim)\n", + " plt.xlabel(\"Training examples\")\n", + " plt.ylabel(\"Score\")\n", + " train_sizes, train_scores, test_scores = learning_curve(\n", + " estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n", + " train_scores_mean = np.mean(train_scores, axis=1)\n", + " train_scores_std = np.std(train_scores, axis=1)\n", + " test_scores_mean = np.mean(test_scores, axis=1)\n", + " test_scores_std = np.std(test_scores, axis=1)\n", + " plt.grid()\n", + "\n", + " plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n", + " train_scores_mean + train_scores_std, alpha=0.1,\n", + " color=\"r\")\n", + " plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n", + " test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n", + " plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n", + " label=\"Training score\")\n", + " plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n", + " label=\"Cross-validation score\")\n", + "\n", + " plt.legend(loc=\"best\")\n", + " return plt" + ] + }, + { + "cell_type": "markdown", + "id": "0k8jI-8u_Fvj", + "metadata": { + "id": "0k8jI-8u_Fvj" + }, + "source": [ + "One thing to note, Logistic and KNN models should use scalers so this is why I'm passing in the X_train_pca variable instead of the X_train. X_train_pca has the Robust scaler and PCA applied to it." + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "LGG0ftb9_JCg", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "LGG0ftb9_JCg", + "outputId": "ca46e452-2922-4aed-c4ac-e2962aace4cb" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "knn = KNeighborsClassifier(n_neighbors=1)\n", + "plot_learning_curve(knn, \"Learning Curve for KNN\", X_train_pca, y_train, cv=5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "S1UEswgP_S4k", + "metadata": { + "id": "S1UEswgP_S4k" + }, + "source": [ + "This learning curve shows a consistently high training line which indicates that the model fits the training data well. But the noticeable gap between the training and cross-validation scores suggests that the model may be overfitting the training data. The wider shading on the cross validation green line suggestion a high variability on how the model performs on unseen data. This shows that there can definitely be improvements on the model to get a better plot, perhaps further feature engineering by removing irrelevant features. We know already that increasing the k neighbors value will not improve due to the higher error rate.\n", + "\n", + "\n", + "Now plot a validation curve for KNN.\n", + "\n", + "#### Validation Curve for KNN\n", + "\n", + "Using the prevoius Validation Curve plot function above let's pass in KNN information to plot for KNN. Keep in mind that I will use X_train_pca again." + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "5pISLcoi_Uh7", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 569 + }, + "id": "5pISLcoi_Uh7", + "outputId": "fd100557-aede-4c26-c5ff-b60b35292dc5" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# KNN model\n", + "model_knn = KNeighborsClassifier()\n", + "param_name_knn = 'n_neighbors'\n", + "param_range_knn = np.arange(1, 21) # Checking values from 1 to 20\n", + "\n", + "# Plotting the validation curve\n", + "plot_validation_curve(model_knn, \"Validation Curve for KNN\", X_train_pca, y_train,\n", + " param_name=param_name_knn, param_range=param_range_knn, cv=5)\n" + ] + }, + { + "cell_type": "markdown", + "id": "TQt0pGbS_mfI", + "metadata": { + "id": "TQt0pGbS_mfI" + }, + "source": [ + "This plot suggest that there might be an underfitting on the model as shown with the convergence at a lower score. As we've seen before when trying different k neighbors value, the training score is high on low k neighbors but the score decreases as the neighbor count increases suggesting the model becomes less flexible and is less able to perfectly fit the training data. The cross-validation score is also decreasing indicating the lower neighbors value is a better fit." + ] + }, + { + "cell_type": "markdown", + "id": "Ud01NL2S_paY", + "metadata": { + "id": "Ud01NL2S_paY" + }, + "source": [ + "# Random Forest\n", + "With Random forest, scaling is not necessary since it is a tree-based model and does not require feature scaling to perform well since it does not utilize distance measures like KNN or logistic regression." + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "WHkkj1OQ_yi6", + "metadata": { + "id": "WHkkj1OQ_yi6" + }, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier" + ] + }, + { + "cell_type": "markdown", + "id": "6X7R7M3U_6dr", + "metadata": { + "id": "6X7R7M3U_6dr" + }, + "source": [ + "Let's resplit the train and testing data using 30% testing data size." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "8PqvO5Ez_5fO", + "metadata": { + "id": "8PqvO5Ez_5fO" + }, + "outputs": [], + "source": [ + "# Splitting data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "id": "ZAeVCKi7AAuK", + "metadata": { + "id": "ZAeVCKi7AAuK" + }, + "source": [ + "Initialize a random forest model and fit it to the training data." + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "id": "L8bWrDR-ADdH", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "id": "L8bWrDR-ADdH", + "outputId": "4deeb367-ccd5-4333-ce38-f12036012986" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestClassifier(random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestClassifier(random_state=42)" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Initialize the Random Forest Classifier\n", + "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n", + "\n", + "# Train the model\n", + "rf_classifier.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "id": "T2Q_6IkBAQcJ", + "metadata": { + "id": "T2Q_6IkBAQcJ" + }, + "source": [ + "Next run a prediction for y values. Print the accuracy score for test and training data and print the classification report, confusion matrix and ROC/AUC curve." + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "id": "dNLDpa7VAUrN", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 790 + }, + "id": "dNLDpa7VAUrN", + "outputId": "784d0ee3-d250-4437-b378-109d400963de" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 93.8402\n", + "Train Accuracy: 100.0\n", + "Test Accuracy: 93.8402\n", + " precision recall f1-score support\n", + "\n", + " 0 0.95 0.93 0.94 939\n", + " 1 0.92 0.95 0.94 863\n", + "\n", + " accuracy 0.94 1802\n", + " macro avg 0.94 0.94 0.94 1802\n", + "weighted avg 0.94 0.94 0.94 1802\n", + "\n", + "Cross-validation scores: [93.46 93.34 91.32 92.75 93.93]\n", + "Mean cross-validation score: 92.96\n", + "Precision scores: 92.306\n", + "Recall scores: 94.02\n", + "F1 scores: 93.15\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Predicting the Test set results\n", + "y_pred = rf_classifier.predict(X_test)\n", + "\n", + "# Print accuracy\n", + "print(\"Accuracy:\", round(accuracy_score(y_test, y_pred) * 100, 4))\n", + "\n", + "# score the model accuracy scores and store in variables for display later\n", + "acc_rf_train = rf_classifier.score(X_train, y_train)\n", + "acc_rf_test = rf_classifier.score(X_test, y_test)\n", + "\n", + "# scores\n", + "print('Train Accuracy:', round(acc_rf_train * 100, 4))\n", + "print('Test Accuracy:', round(acc_rf_test * 100, 4))\n", + "\n", + "# Classification report\n", + "print(classification_report(y_test, y_pred))\n", + "\n", + "#Cross Validation\n", + "# Perform 5-fold cross-validation\n", + "scores = cross_val_score(rf_classifier, X_train, y_train, cv=5, scoring='accuracy').round(4) * 100\n", + "print(\"Cross-validation scores:\", scores)\n", + "print(\"Mean cross-validation score:\", scores.mean().round(4))\n", + "precision_scores = cross_val_score(rf_classifier, X_train, y_train, cv=5, scoring='precision').round(4) * 100\n", + "print(\"Precision scores:\", precision_scores.mean().round(4))\n", + "recall_scores = cross_val_score(rf_classifier, X_train, y_train, cv=5, scoring='recall').round(4) * 100\n", + "print(\"Recall scores:\", recall_scores.mean().round(4))\n", + "f1_scores = cross_val_score(rf_classifier, X_train, y_train, cv=5, scoring='f1').round(4) * 100\n", + "print(\"F1 scores:\", f1_scores.mean().round(4))\n", + "\n", + "# Confusion matrix\n", + "conf_mat = confusion_matrix(y_test, y_pred)\n", + "plt.figure(figsize=(3, 2))\n", + "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')\n", + "plt.xlabel('Predicted Labels')\n", + "plt.ylabel('Actual Labels')\n", + "plt.title('Confusion Matrix')\n", + "plt.show()\n", + "\n", + "# ROC Curve and AUC\n", + "y_prob = rf_classifier.predict_proba(X_test)[:, 1]\n", + "fpr, tpr, thresholds = roc_curve(y_test, y_prob)\n", + "roc_auc = auc(fpr, tpr)\n", + "\n", + "plt.figure(figsize=(3, 2))\n", + "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n", + "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n", + "plt.xlim([0.0, 1.05])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "TGIeq0AuAd3m", + "metadata": { + "id": "TGIeq0AuAd3m" + }, + "source": [ + "Random Forest is a big jump in accuracy than the previous models we've done with our baseline, PCA or KNN. Although 100% training accuracy is a great score, I wonder if it's overfitting it. The ROC curve area being above .9 at .98 is excellent too.\n", + "\n", + "\n", + "#### Model Evaluation Metrics Table\n", + "***Model Added: 9. Random Forest with estimator 100***\n", + "\n", + "| Number | Model | Accuracy | Training | Precision-0 | Precision-1 | Recall-0 | Recall-1 | F1-0 | F1-1 | Mean | Precision | Recall | F1 | ROC |\n", + "|--------|-------|----------|----------|-------------|-------------|-----------|-----------|-------|-------|-------|-----------|--------|------|-----|\n", + "| 1 | Baseline - Logistic Regression Model with Robust scaled 25% test data set | 86.8842 | 87.611 | 86 | 88 | 89 | 85 | 87 | 86 | 86.39 | 88.142 | 84.376 | 86.218 | 0.93|\n", + "| 2 | Baseline with 98% PCA | 86.6178 | 86.5897 | 85 | 88 | 89 | 84 | 87 | 87 | 86.124 | 88.006 | 83.936 | 85.916 | 0.92|\n", + "| 3 | Baseline with 95% PCA | 86.0693 | 87.8553 | 87 | 84 | 93 | 72 | 90 | 77 | 87.37 | 87.692 | 70.002 | 77.83 | 0.92|\n", + "| 4 | Baseline with 93% PCA | 85.8189 | 85.857 | 85 | 87 | 88 | 83 | 87 | 86 | 85.236 | 88.052 | 81.868 | 84.836 | 0.92 |\n", + "| 5 | KNN k=5 with 98% PCA k=5 | 79.9601 | 87.944 | 82 | 78 | 78 | 82 | 80 | 80 | 79.372 | 80.856 | 77.462 | 79.106 | 0.89|\n", + "| 6 | KNN k=5 with 95% PCA k=5 | 79.9601 | 88.2327 | 81 | 79 | 79 | 81 | 80 | 80 | 79.308 | 80.99 | 77.066 | 78.974 | 0.89|\n", + "| 7 | KNN k=5 with 93% PCA k=5 | 79.8269 | 87.9218 | 82 | 78 | 78 | 82 | 80 | 80 | 79.218 | 80.88 | 77.022 | 78.896 | 0.88|\n", + "| 8 | KNN k=1 with 98% PCA k=5 | 89.281 | 100.0 | 93 | 86 | 85 | 94 | 89 | 89 | 77.886 | 84.702 | 68.616 | 75.798 | 0.87 |\n", + "| 9 | Random Forest estimator=100 | 93.8402 | 100.0 | 95 | 92 | 93 | 95 | 94 | 94 | 92.96 | 92.306 | 94.02 | 93.15 | 0.98 |\n", + "\n", + "
\n", + "
\n", + "\n", + "Let's see if we can hyperparameter tune this using GridSearch and tuning n_estimator, max_features, max_depth and criterion." + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "QVLUGTLwAlqn", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QVLUGTLwAlqn", + "outputId": "3ed709aa-8761-4e17-c9c3-7c738a1337b7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters found: {'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'n_estimators': 300}\n", + "Best score found: 88.654\n" + ] + } + ], + "source": [ + "# Define parameter grid\n", + "param_grid = {\n", + " 'n_estimators': [100, 200, 300],\n", + " 'max_features': ['auto', 'sqrt', 'log2'],\n", + " 'max_depth' : [4,6,8,10],\n", + " 'criterion' :['gini', 'entropy']\n", + "}\n", + "\n", + "# Initialize the GridSearchCV object\n", + "grid_search = GridSearchCV(estimator=rf_classifier, param_grid=param_grid, cv=5)\n", + "\n", + "# Fit GridSearch to the training data\n", + "grid_search.fit(X_train, y_train)\n", + "\n", + "# Printing the best parameters and best score\n", + "print(\"Best parameters found: \", grid_search.best_params_)\n", + "print(\"Best score found: \", round(grid_search.best_score_ * 100, 4))" + ] + }, + { + "cell_type": "markdown", + "id": "OPJIPLmlA24B", + "metadata": { + "id": "OPJIPLmlA24B" + }, + "source": [ + "Using hyperparameter tunning it gave us the best parameters that will give the best score. And that's using 300 for n_estimators, sqrt max features, 10 for max depth and gini criterion. But even with this the best score it gives is 88.65% accuracy which still doesn't top our the initial fitting of random forest with n_estimator = 100 giving a score of 93.84.\n", + "\n", + "Let's plot learning and validation curves for Random Forest.\n", + "\n", + "#### Learning Curve for Random Forest\n", + "\n", + "Let's reuse that learning curve plot.\n", + "\n", + "Here we will just use the training data X_train and y_train instead of the scaled or pca training data. Random Forest does not need scaling of data." + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "id": "j-O3tkttCf2n", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "j-O3tkttCf2n", + "outputId": "2d27bfd5-72a4-4fcf-b992-c774f7302f60" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = RandomForestClassifier(n_estimators=100)\n", + "plot_learning_curve(model, \"Random Forest Learning Curve\", X_train, y_train, cv=5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fbarYr81EaYP", + "metadata": { + "id": "fbarYr81EaYP" + }, + "source": [ + "This Random Forest model is able to almost perfectly fit the training data, capturing the underlying patterns effectively with the training score consistently high (at 100%) regardless of training size. The cross validation score behaves as expected, increasing with more training data. The wide gap beween the two suggest an overfitting issue but it shows how it's sharply converging towards a higher score. A remedy might be to expose the model to a more varied data and reduce irrelevant features. \n", + "\n", + "\n", + "Now let's look at the validation curve.\n", + "\n", + "#### Validation Curve for Random Forest\n", + "\n", + "Using again the validation curve plotting function from earlier and passing in training data without scaling or pca." + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "wxhRyTlgEjAQ", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 569 + }, + "id": "wxhRyTlgEjAQ", + "outputId": "256a762b-5405-4b54-f919-7d344d14d23b" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Example data setup\n", + "X, y = np.random.rand(100, 10), np.random.randint(0, 2, 100)\n", + "\n", + "# Example model setup\n", + "model = RandomForestClassifier(n_estimators=100, random_state=42)\n", + "\n", + "# Define the parameter range\n", + "param_range = np.arange(1, 11) # Max depths from 1 to 10\n", + "\n", + "# Plot the validation curve\n", + "plot_validation_curve(model, \"Validation Curve for RandomForest: max_depth\",\n", + " X_train, y_train, param_name=\"max_depth\", param_range=param_range, cv=5)\n" + ] + }, + { + "cell_type": "markdown", + "id": "HnHHZOXBEs4T", + "metadata": { + "id": "HnHHZOXBEs4T" + }, + "source": [ + "The divergence towards the end of the plot as the max_depth increases suggests an overfitting at high depths. The ideal max_depth seems to be slightly beyond the 75th percent area of the range perhaps around max_depth of 6-8." + ] + }, + { + "cell_type": "markdown", + "id": "hXQUItV0Fva-", + "metadata": { + "id": "hXQUItV0Fva-" + }, + "source": [ + "# Decision Tree\n", + "No scaling is necessary for Decision Tree models because Decision Trees do not rely on distance calculations between features. They work by making splits based on feature values, and scaling does not influence the ability of the tree to partition data.\n", + "\n", + "PCA is not necessary also since it would obscure the tracing ability of the features becasue the new features from PCA are linear combinations of the original features.\n", + "\n", + "Resplit the data for trainin keeping it at 30% testing size.\n", + "\n", + "Reset the X, y, train and test data variables." + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "H2ITyl4HF1RU", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "H2ITyl4HF1RU", + "outputId": "c95c1800-4ef6-42df-d423-518f9d1bfd16" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X shape (6006, 104)\n", + "y shape (6006,)\n" + ] + } + ], + "source": [ + "X = data.drop('TargetEncoded', axis=1)\n", + "y = data['TargetEncoded']\n", + "print('X shape', X.shape)\n", + "print('y shape', y.shape)\n", + "# Split the data into training and testing sets using 20% for testing\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "id": "DlyWmKnQF7VJ", + "metadata": { + "id": "DlyWmKnQF7VJ" + }, + "source": [ + "Fit the model on the training data." + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "4GztaMKcF8VB", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "id": "4GztaMKcF8VB", + "outputId": "876fb42e-2318-4d3f-bf5c-15440471a686" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
DecisionTreeClassifier(random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "DecisionTreeClassifier(random_state=42)" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.tree import DecisionTreeClassifier\n", + "# Initialize and train the Decision Tree\n", + "tree_model = DecisionTreeClassifier(random_state=42)\n", + "tree_model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "id": "HieC2WjDGHZO", + "metadata": { + "id": "HieC2WjDGHZO" + }, + "source": [ + "Predict the target values and display the evaluation metrics." + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "63R8Zk5qGMBA", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 944 + }, + "id": "63R8Zk5qGMBA", + "outputId": "200a37ed-1798-4106-cd7d-3489509abbd3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 89.3452\n", + "Train Accuracy: 100.0\n", + "Test Accuracy: 89.3452\n", + " precision recall f1-score support\n", + "\n", + " 0 0.94 0.85 0.89 939\n", + " 1 0.85 0.94 0.89 863\n", + "\n", + " accuracy 0.89 1802\n", + " macro avg 0.90 0.90 0.89 1802\n", + "weighted avg 0.90 0.89 0.89 1802\n", + "\n", + "Cross-validation scores: [85.02 87.99 86.44 88.82 86.67]\n", + "Mean cross-validation score: 86.988\n", + "Precision scores: 84.126\n", + "Recall scores: 91.822\n", + "F1 scores: 87.79\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Predict on the testing set\n", + "y_pred = tree_model.predict(X_test)\n", + "\n", + "# Print accuracy\n", + "print(\"Accuracy:\", round(accuracy_score(y_test, y_pred) * 100, 4))\n", + "\n", + "# score the model accuracy scores and store in variables for display later\n", + "acc_dtree_train = tree_model.score(X_train, y_train)\n", + "acc_dtree_test = tree_model.score(X_test, y_test)\n", + "\n", + "# scores\n", + "print('Train Accuracy:', round(acc_dtree_train * 100, 4))\n", + "print('Test Accuracy:', round(acc_dtree_test * 100, 4))\n", + "\n", + "# Classification report\n", + "print(classification_report(y_test, y_pred))\n", + "\n", + "#Cross Validation\n", + "# Perform 5-fold cross-validation\n", + "scores = cross_val_score(tree_model, X_train, y_train, cv=5, scoring='accuracy').round(4) * 100\n", + "print(\"Cross-validation scores:\", scores)\n", + "print(\"Mean cross-validation score:\", scores.mean().round(4))\n", + "precision_scores = cross_val_score(tree_model, X_train, y_train, cv=5, scoring='precision').round(4) * 100\n", + "print(\"Precision scores:\", precision_scores.mean().round(4))\n", + "recall_scores = cross_val_score(tree_model, X_train, y_train, cv=5, scoring='recall').round(4) * 100\n", + "print(\"Recall scores:\", recall_scores.mean().round(4))\n", + "f1_scores = cross_val_score(tree_model, X_train, y_train, cv=5, scoring='f1').round(4) * 100\n", + "print(\"F1 scores:\", f1_scores.mean().round(4))\n", + "\n", + "# Confusion Matrix\n", + "conf_mat = confusion_matrix(y_test, y_pred)\n", + "plt.figure(figsize=(3, 2))\n", + "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')\n", + "plt.xlabel('Predicted Labels')\n", + "plt.ylabel('Actual Labels')\n", + "plt.title('Confusion Matrix')\n", + "plt.show()\n", + "\n", + "# ROC Curve and AUC\n", + "y_prob = tree_model.predict_proba(X_test)[:, 1]\n", + "fpr, tpr, thresholds = roc_curve(y_test, y_prob)\n", + "roc_auc = auc(fpr, tpr)\n", + "\n", + "plt.figure(figsize=(6, 4))\n", + "plt.plot(fpr, tpr, label='ROC Curve (area = %0.2f)' % roc_auc)\n", + "plt.plot([0, 1], [0, 1], 'k--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "jpakvptRGWwl", + "metadata": { + "id": "jpakvptRGWwl" + }, + "source": [ + "100% training accuracy is great but testing accuracy of 89.3452% still does not top the Random Forest score. THe sharp rise in the ROC graph indicates that the model has a very good measure of separability between the positive and negative classes.\n", + "\n", + "\n", + "\n", + "#### Model Evaluation Metrics Table\n", + "***Model Added: 10. Decision Tree***\n", + "\n", + "| Number | Model | Accuracy | Training | Precision-0 | Precision-1 | Recall-0 | Recall-1 | F1-0 | F1-1 | Mean | Precision | Recall | F1 | ROC |\n", + "|--------|-------|----------|----------|-------------|-------------|-----------|-----------|-------|-------|-------|-----------|--------|------|-----|\n", + "| 1 | Baseline - Logistic Regression Model with Robust scaled 25% test data set | 86.8842 | 87.611 | 86 | 88 | 89 | 85 | 87 | 86 | 86.39 | 88.142 | 84.376 | 86.218 | 0.93|\n", + "| 2 | Baseline with 98% PCA | 86.6178 | 86.5897 | 85 | 88 | 89 | 84 | 87 | 87 | 86.124 | 88.006 | 83.936 | 85.916 | 0.92|\n", + "| 3 | Baseline with 95% PCA | 86.0693 | 87.8553 | 87 | 84 | 93 | 72 | 90 | 77 | 87.37 | 87.692 | 70.002 | 77.83 | 0.92|\n", + "| 4 | Baseline with 93% PCA | 85.8189 | 85.857 | 85 | 87 | 88 | 83 | 87 | 86 | 85.236 | 88.052 | 81.868 | 84.836 | 0.92 |\n", + "| 5 | KNN k=5 with 98% PCA k=5 | 79.9601 | 87.944 | 82 | 78 | 78 | 82 | 80 | 80 | 79.372 | 80.856 | 77.462 | 79.106 | 0.89|\n", + "| 6 | KNN k=5 with 95% PCA k=5 | 79.9601 | 88.2327 | 81 | 79 | 79 | 81 | 80 | 80 | 79.308 | 80.99 | 77.066 | 78.974 | 0.89|\n", + "| 7 | KNN k=5 with 93% PCA k=5 | 79.8269 | 87.9218 | 82 | 78 | 78 | 82 | 80 | 80 | 79.218 | 80.88 | 77.022 | 78.896 | 0.88|\n", + "| 8 | KNN k=1 with 98% PCA k=5 | 89.281 | 100.0 | 93 | 86 | 85 | 94 | 89 | 89 | 77.886 | 84.702 | 68.616 | 75.798 | 0.87 |\n", + "| 9 | Random Forest estimator=100 | 93.8402 | 100.0 | 95 | 92 | 93 | 95 | 94 | 94 | 92.96 | 92.306 | 94.02 | 93.15 | 0.98 |\n", + "| 10 | Decision Tree | 89.3452 | 100.0 | 94 | 85 | 85 | 94 | 89 | 89 | 86.988 | 84.126 | 91.822 | 87.79 | 0.90|\n", + "\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "Let's try some hyperparameter tunning." + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "YdaRJVWeGc2c", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YdaRJVWeGc2c", + "outputId": "0c6ab739-92ae-4644-c2db-11dc1a37df8d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: {'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2}\n", + "Best cross-validation score: 87.87\n" + ] + } + ], + "source": [ + "# Set up the parameters to tune\n", + "param_grid = {\n", + " 'max_depth': [None, 10, 20, 30, 40, 50],\n", + " 'min_samples_split': [2, 10, 20],\n", + " 'min_samples_leaf': [1, 5, 10],\n", + " 'criterion': ['gini', 'entropy']\n", + "}\n", + "\n", + "# GridSearchCV for finding the best parameters\n", + "grid_search = GridSearchCV(DecisionTreeClassifier(random_state=42), param_grid, cv=5)\n", + "grid_search.fit(X_train, y_train)\n", + "\n", + "# Best parameters and score\n", + "print(\"Best parameters:\", grid_search.best_params_)\n", + "print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_ * 100))\n" + ] + }, + { + "cell_type": "markdown", + "id": "SmaW5Z3wGiQ1", + "metadata": { + "id": "SmaW5Z3wGiQ1" + }, + "source": [ + "This hyperparameter tuning gave the best score of 87.87% having entropy for criterion, None for max_depth, 1 min leaf sample, 2 min samples split. But the best score is still lower than our initial score with decision tree at 89.3452.\n", + "\n", + "Let's plot learning and validation curve for Decision Tree model.\n", + "\n", + "#### Learning Curve for Decision Tree" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "lAHbR_btGkxL", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "lAHbR_btGkxL", + "outputId": "e71a61a6-5fd8-415e-ee59-a948c08b0b65" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "decision_tree = DecisionTreeClassifier(random_state=42)\n", + "plot_learning_curve(decision_tree, \"Learning Curve for Decision Tree\", X_train, y_train, cv=5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "mt_y8PrjGqaw", + "metadata": { + "id": "mt_y8PrjGqaw" + }, + "source": [ + "The convergence is a good sign but the wide gap is an issue here. The training did well here. Adjusting the max depth gave the best result with None as the hyperparameter tuning proved.\n", + "\n", + "Let's plot the validation curve for Decision Tree now." + ] + }, + { + "cell_type": "markdown", + "id": "Z422xanmG64G", + "metadata": { + "id": "Z422xanmG64G" + }, + "source": [ + "# Validation Curve with Decision Tree\n", + "Let's plot one using Decisiont Tree since we hyperparameterized it in one of our analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "yXJJsj65G92G", + "metadata": { + "id": "yXJJsj65G92G" + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import validation_curve\n", + "\n", + "# Range of values for the hyperparameter 'max_depth'\n", + "param_range = np.arange(1, 20)\n", + "\n", + "# Calculate accuracy on training and test set using different values of max_depth\n", + "train_scores, test_scores = validation_curve(\n", + " DecisionTreeClassifier(),\n", + " X,\n", + " y,\n", + " param_name=\"max_depth\",\n", + " param_range=param_range,\n", + " cv=10, # 10-fold cross-validation\n", + " scoring=\"accuracy\",\n", + " n_jobs=-1\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "lpBxO0iPHE-V", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 564 + }, + "id": "lpBxO0iPHE-V", + "outputId": "ddd2dc49-b2ab-4d40-9350-6547d2159cc5" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate mean and standard deviation for training set scores\n", + "train_mean = np.mean(train_scores, axis=1)\n", + "train_std = np.std(train_scores, axis=1)\n", + "\n", + "# Calculate mean and standard deviation for test set scores\n", + "test_mean = np.mean(test_scores, axis=1)\n", + "test_std = np.std(test_scores, axis=1)\n", + "\n", + "# Plot the validation curve\n", + "plt.figure(figsize=(8, 6))\n", + "plt.title(\"Validation Curve with Decision Tree\")\n", + "plt.xlabel(\"Max Depth of the Tree\")\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.ylim(0.5, 1.1) # Extend the y axis for better visualization\n", + "\n", + "# Plot training scores\n", + "plt.plot(param_range, train_mean, label=\"Training score\",\n", + " color=\"darkorange\", lw=2)\n", + "plt.fill_between(param_range, train_mean - train_std,\n", + " train_mean + train_std, color=\"darkorange\", alpha=0.2)\n", + "\n", + "# Plot cross-validation scores\n", + "plt.plot(param_range, test_mean, label=\"Cross-validation score\",\n", + " color=\"navy\", lw=2)\n", + "plt.fill_between(param_range, test_mean - test_std,\n", + " test_mean + test_std, color=\"navy\", alpha=0.2)\n", + "\n", + "plt.legend(loc=\"best\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "K8gFLphCHItK", + "metadata": { + "id": "K8gFLphCHItK" + }, + "source": [ + "This Decision Tree validation curve plot shows how changes in the max dept hyperparameter affects the model's accuracy both on the training set (orange line) and on the cross-validation set (blue line).\n", + "\n", + "The training score performs better as the maximum depth of the tree increases. The model becomes more capable of learning complex patterns in the training data, which generally increases accuracy on the training set. This curve keeps increasing with slight platueaing telling us that the modes becomes better fitted to the training data as complexity grows. This is indicative of the 100% accuracy on the training accuracy score we observed when running the Decision Tree model.\n", + "\n", + "For Cross-validtion score line the increasing gap between the lines proves it might be overfitting with higher depths. The optimal depth of the tree, where the validation accuracy is maximized while avoiding overfitting, appears to be around the range of 10 to 12.5." + ] + }, + { + "cell_type": "markdown", + "id": "p3R3jAGaHY8s", + "metadata": { + "id": "p3R3jAGaHY8s" + }, + "source": [ + "# Gradient Boosting Classifier\n", + "Much like the Decision Tree model, the Gradient Boosting Classifier does not need scaling on the training data.\n", + "Let's resplit and reset the train data variables." + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "EaGoZk8jHeSC", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EaGoZk8jHeSC", + "outputId": "285b467b-52b9-4153-c425-ca2c85eec0d4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X shape (6006, 104)\n", + "y shape (6006,)\n" + ] + } + ], + "source": [ + "X = data.drop('TargetEncoded', axis=1)\n", + "y = data['TargetEncoded']\n", + "print('X shape', X.shape)\n", + "print('y shape', y.shape)\n", + "# Split the data into training and testing sets using 20% for testing\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)" + ] + }, + { + "cell_type": "markdown", + "id": "xMOD8XfjHifm", + "metadata": { + "id": "xMOD8XfjHifm" + }, + "source": [ + "Let's fit a Gradient Boosting model and predict the y target values." + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "8G-BD_F7HoAZ", + "metadata": { + "id": "8G-BD_F7HoAZ" + }, + "outputs": [], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "gbm = GradientBoostingClassifier(random_state=42)\n", + "gbm.fit(X_train, y_train)\n", + "\n", + "# Predictions\n", + "y_pred = gbm.predict(X_test)" + ] + }, + { + "cell_type": "markdown", + "id": "wwHDfpCKHqsO", + "metadata": { + "id": "wwHDfpCKHqsO" + }, + "source": [ + "Then let's print the accuracy metric scores for trianina and test data as well as the classification, confusion matrix and ROC." + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "id": "PaaEXD1bHr4j", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "PaaEXD1bHr4j", + "outputId": "65d616fc-428d-442b-dfc2-94ac3d2d161d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy: 87.4584\n", + "Train Accuracy: 89.7003\n", + "Test Accuracy: 87.4584\n", + "Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.87 0.89 0.88 939\n", + " 1 0.88 0.86 0.87 863\n", + "\n", + " accuracy 0.87 1802\n", + " macro avg 0.87 0.87 0.87 1802\n", + "weighted avg 0.87 0.87 0.87 1802\n", + "\n", + "Cross-validation scores: [89.3 87.63 85.73 85.97 88.93]\n", + "Mean cross-validation score: 87.512\n", + "Precision scores: 89.35\n", + "Recall scores: 85.702\n", + "F1 scores: 87.482\n", + "Confusion Matrix:\n", + " [[833 106]\n", + " [120 743]]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ROC-AUC Score: 0.9385894118271331\n" + ] + }, + { + "data": { + "image/png": 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b21BTU5MpfDMzM7F3716Z5VxdXaGqqorNmzejVatWRW6vqOPfrVs3HDx4EGKxGC1atCh2Pnno6+ujT58+ePv2Lby8vPDy5UvUrVtXeml4cb4v3NzccODAAezatYtO1SghKkYIp6pUqYLZs2dj5syZ2L9/P4YMGYItW7bAzc0NnTt3xogRI1CtWjV8+vQJ0dHRuHPnDv78808AwKJFi3D27Fm0bdsWc+bMQYMGDZCUlIRz587B29sbderUgZeXF44ePYq2bdti6tSpaNiwISQSCWJjY3HhwgVMmzbtu7+AR40aBU9PT7x79w5OTk4FCp9FixYhODgYTk5OmDx5MmrXro2srCy8fPkSZ86cQWBgYIm70Pfs2YOOHTvC1dW10EnP6tSpU+jlq0ZGRmjfvj1+//136dU0jx49kvmQVkTu/Ms8J0yYgD59+uD169dYvHgxzMzMCsys26BBA1y8eBGnTp2CmZkZdHR0iiwii2v48OFYt24dhgwZgiVLlsDW1hZnz57F+fPnAeC7f0Hb2NhIe70aN24snfQMyJt0a+fOnWCMyV2MyHNcitK1a1f4+vpi0KBB8PDwQGJiItasWVNgbhdra2vMmTMHixcvRmZmJgYOHAg9PT1ERUUhISFBeultgwYNcOzYMWzevBkODg5QUVGBo6MjBgwYgH379sHd3R1TpkxB8+bNwefz8ebNG4SEhODnn3+W+/0DQPfu3aXzChkbG+PVq1dYv349rKyspFeQNWjQAACwYcMGDB8+HHw+H7Vr1y7QGwPkjQMKCgrCuHHj8PjxY7i4uEAikeDmzZuwt7fHgAED5M5IyhFux8+SyiL/apqvr2phjLHMzExmaWnJatWqxUQiEWOMsXv37rF+/fqxqlWrMj6fz0xNTVn79u1ZYGCgzLqvX79mo0aNYqampozP5zNzc3PWr18/9v79e+kyaWlpbN68eax27dpMIBAwPT091qBBAzZ16lSZK06+vhogX3JyMtPQ0PjmSP6PHz+yyZMnMxsbG8bn85mBgQFzcHBgc+fOZWlpaYyx/12Vsnr1armOXVpaGlu2bBlr3Lgx09TUZJqamqxhw4ZsyZIl0m1/CQCbOHEiCwgIYDVr1mR8Pp/VqVOH7du3r1Ryr1ixgllbWzOhUMjs7e3Ztm3bmI+PD/v618vdu3dZ69atmaamJgMgvVKiqKtptLS0CuyrsO3Gxsay3r17M21tbaajo8N++eUXdubMGQaA/fXXX988tvmeP3/OJkyYwGxtbZlQKGQaGhqsbt26zNvbW+ZKD2dnZ1avXr0C6w8fPrzAlSrFPS75/1+F2blzJ6tduzYTCoWsRo0abPny5WzHjh2FXoGyZ88e1qxZM6aurs60tbVZkyZNZK4m+vTpE+vTpw/T19dnPB5PJkdubi5bs2YNa9SokXT9OnXqME9PT/b06VPpclZWVqxr166FZv3652ft2rXMycmJGRkZMYFAwCwtLdno0aPZy5cvZdabPXs2Mzc3ZyoqKjLfB19fTcNY3u+K+fPns1q1ajGBQMAMDQ1Z+/bt2bVr1wrNRCoOmg6eECXD4/EwceJE+Pv7cx2FM8uWLcO8efMQGxtb4l4pQkjZodM0hJAKLb/oqlOnDnJzc/Hff//Bz88PQ4YMoUKEkAqCihFCSIWmqamJdevW4eXLl8jOzoalpSV+++03zJs3j+tohJBiotM0hBBCCOEUXaxNCCGEEE5RMUIIIYQQTlExQgghhBBOVboBrBKJBO/evYOOjk6xpzUmhBBCSN6sxKmpqQq/T1ClK0bevXtXovs3EEIIISTP69evFXrpfKUrRvKnGX79+jV0dXU5TkMIIYRUHCkpKahevXqhU/b/iEpXjOSfmtHV1aVihBBCCCkBRQ9zoAGshBBCCOEUFSOEEEII4RQVI4QQQgjhFBUjhBBCCOEUFSOEEEII4RQVI4QQQgjhFBUjhBBCCOEUFSOEEEII4RSnxcjly5fRvXt3mJubg8fj4cSJE99d59KlS3BwcIC6ujpq1KiBwMDA0g9KCCGEkFLDaTGSnp6ORo0awd/fv1jLx8TEwN3dHW3atEFERATmzJmDyZMn4+jRo6WclBBCCCGlhdPp4N3c3ODm5lbs5QMDA2FpaYn169cDAOzt7REeHo41a9bgl19+KXSd7OxsZGdnS5+npKT8UGZCCCEFSSQMt15+QnJmbqnv6/6bJDx9nwYhX7XU90Vk5WSklcp2K9S9aa5fvw5XV1eZts6dO2PHjh3Izc0Fn88vsM7y5cuxcOHCsopICCGlQiSWIOzlZ2Tmikq8jftvkvM+xNUU3yn+1713EEuYwrdLygdxehI+/bcNWnWdS2X7FaoYiY+Ph4mJiUybiYkJRCIREhISYGZmVmCd2bNnw9vbW/o8/46DhBBS3iw6FYWdV2OgKSj4F39GjpiDRCXjYFWl1PcRn5yFXxwsUEWz4B+hRHEYYwg9fRh/7l6BjNRk6KS9LpX9VKhiBCh4p0DGWKHt+YRCIYRCYannIoRULjEJ6YhLzvzmMhnZYvx9/x00hcX7Vbv/Zmzeet8pPBpZ6BUvZCHeJWehn6MF9DUEJd5GUdT5KujW0BxVtBS/bVL2Hj9+DE9PT1y6dAkA0LhxY6xbtw4uLi4K31eFKkZMTU0RHx8v0/bhwweoqanB0NCQo1SEkIrmY2o2Xiamf3e5x/GpePguGQJV2dMaT96n4fqLxNKKh0MeLWGur1GgXVOgCkNt+uOKlK7s7GysXLkSS5cuRU5ODjQ1NbFo0SJMmTIFGRkZpbLPClWMtGrVCqdOnZJpu3DhAhwdHQsdL0IIqTxSs3Lx7EPhg+skDIh8k4TXnzORkSPCgVuK62q2M9H+5uuZuWJU1VGHs51xsbbXqLo+WtSgP64Id86cOQMfHx8AeReaBAQEwNraulT3yWkxkpaWhmfPnkmfx8TE4O7duzAwMIClpSVmz56Nt2/fYs+ePQCAcePGwd/fH97e3hg7diyuX7+OHTt24MCBA1y9BUKIHD6kZOFDavb3FyxCapYIF6LiIfhqAObWyy/ASjB2soaR1neXefM5E784WMBYW/bUg4oKD90amsO26reLEUIqAsaYdLhDz549MWzYMLi7u6Nfv35FDoNQJB5jJfkRVoyLFy8Weu5p+PDh2LVrF0aMGIGXL1/i4sWL0tcuXbqEqVOn4uHDhzA3N8dvv/2GcePGFXufKSkp0NPTQ3JyMnR1dRXxNgipFFKzcvE2qeAYifMP3iNXLJE+P/cwHunZogJXbLxMLJ3u3a+Z66lDVbXgL08tgRqaWRtAWz3vb7B2dsbUA0EqPcYYDh8+jOXLlyMkJARVqnx78HFpfYZyWoxwgYoRQr7tXVImgqPe41N6DrZcfg6LKprIzBEXWoiUlJmeeonXTcsSoVoVDbSpZSTTrilQw+g2NtBVp1O2hBTHq1evMGHCBJw5cwYAMG/ePCxevPib65TWZ2iFGjNCCFGMj6nZyMj533wVyZm5uPj4Iw6Hv8abz7JFx9fjMIy+Ol0hYUBKZi6GtLSStmXlitGjkXmB0ykqKjw0qKYHvirdFosQrohEIvj5+eH3339HRkYGBAIB5syZg1mzZnGWiYoRQpTEo/gUXHr88ZvL7LsZi9hPxT9dMqiFJXTV+dLBl/Wr6UKHeh4IqbBu374NDw8P3LlzBwDQtm1bbNmyBXXq1OE0FxUjhFQgjDEkpOXg67OrH1Kz0W3jFbm3p/XF5FrpOWLUMNJC+zpV4elcE8Y6dAkpIcpm06ZNuHPnDvT19bFmzRqMHDkSKirc91RSMUJIOZEtEiM9W4zYTxmYuO8OqhUyz8Stl5++ux1nO2MYahc96RRjQP9m1dHM2gCqKqU/Sp4Qwq3s7Gzp5J+rV68Gn8/HokWLCsxoziUawEqIgkW+Sca6f54gK7f403cnpuXg8ftUufbzdR0hYYBn2xqY7W4v13YIIcopLi5OOlHZqVOnFHKJLg1gJaScyhFJIPmipu/uL//pksJ0tDdB76bVCrRXr6KJBj8wHTghRLlJJBJs374dM2fORHJyMlRVVREZGYmGDRtyHa1IVIwQUoRcsQShTz8iJbPgXVJjEtJx/Xkiwl59KnKyrbZ2xvilkGKiKDweD61qGMJQSwAer+j7LRFCSFGioqLg4eGBq1evAgAcHBywbdu2cl2IAFSMEFIokVgCr0N3cfp+XInW1xSoYsdwR7qElRBSJrKzs7F06VKsWLECubm50NLSwtKlSzFp0iSoqha8C3R5Q8UIqZSevE/FwK03kJieU6zlv55gCwCSMnLR2tYI9mY6aFvLWGZODU2BKvVsEELKjEQiwf79+5Gbm4tu3bph06ZNsLS05DpWsVExQiqV5Ixc/Hn7NZacji72Oqcn/4R65jRGgxBSvnz+/Bm6urpQVVWFhoYGduzYgY8fP+KXX36pcH8MUTFClFZWrhj+/z3D+5Qsaduft9/ILDPmJxuMa1ez0PUFaio0tTghpNxhjOHAgQPw8vLCvHnzMHnyZACAs7Mzx8lKjooRohQyckSYd+IB0rJEuBD1HjrqakjNKjjwNJ+eBh+Lfq6HnxsXf4ApIYRw7cWLFxg/fjwuXLgAANi/fz8mTZpULiYu+xFUjJAKQSSW4P7bZIjEBS9dScnMxZg94TJtXxciv3X531THNkZa6FLftHSCEkJIKcjNzcW6deuwYMECZGZmQigU4vfff8eMGTMqfCECUDFCKgCJhGHgthsIe/n5u8sK1VQwzrkmGlvqw9pQC2oqPFhU0ahw508JISTfvXv3MGzYMNy/fx8A4OLigsDAQNjZ2XGcTHGoGCHlEmMMm0KeYde1V0hIy5Z5rYaxVoHlxRKGgc0tMc658PEfhBBSUfF4PERFRcHAwABr167F8OHDle4PLCpGCOcYY7gZ8wn/Rr/HrZhPeJuUVaAAAQAeD7g2qz3M9Ares4UQQpTJ48ePUbt2bQBAw4YNsX//frRr1w7GxsYcJysdVIwQTiWkZaPL+tBCi498/oOaoHF1fVhU0SzDZIQQUvbevXuHyZMn48SJE7hz54505tS+fftynKx0UTFCSl22SIy4pCzcjEnEP9Ef8OBtMvQ08i6ZfRQve3O41raGsDTIG2BqaaAJG6OCp2QIIUTZSCQSBAYGYvbs2UhJSYGqqipu3LhR7qdxVxQqRkipkkgYWq8IKdDzEZecJfPc0kATpyb9BD1NmteDEFK5REZGwsPDAzdu3AAAtGjRAlu3bq00hQhAxQgpJc8+pGHhqYcIfZogbVPh5d3mvkcjc3SwrwojbSEAQFedj/rVdJVuQBYhhHzPsmXL4OPjA5FIBB0dHSxbtgzjx4+vEPeTUSQqRojCSCQMnzNycOZBPH4/8aDA68+XuVPBQQghX9DW1oZIJELPnj2xceNGWFhYcB2JE1SMEIUZsuMmrj1PlGlztKoCb1c7tLAxpEKEEFLpJSQk4O3bt2jUqBEAYOLEiahTpw5cXV05TsYtKkZIiWXkiPAyIQORb5PAAw83XsgWIgt71MOgFpbgq1b82QEJIeRHMMbwxx9/YOrUqdDX10dkZCQ0NDSgqqpa6QsRgIoRIodcsQSzj0UiIS0bFx9/LHK58HkdpeNBCCGksnv27BnGjRuHf//9FwBgbm6O+Ph42NjYcJys/KBihBSJMQYJA26+SMTV5wnYFPK8yGXb1DICX1UFTarrUyFCCCHIu5/MmjVrsGjRImRlZUFdXR0+Pj6YNm0a+Hy6cvBLVIyQQr1MSEefwGtISMsp9PWVvzSApYEWGlXXg7qaKlRUaDwIIYTk+/TpE5ydnfHgQd5g/k6dOmHz5s2oWZNuWVEYKkYqubRsEW7FJOLqs0TEp2RB7f+Lir/uviuwrFNNQ3i0rYF2tauWdUxCCKlQqlSpAisrK8THx2PdunUYPHgwDeL/BipGKqmbLxLRf+uN7y7X2tYQAYMdpDOmEkIIKYgxhhMnTqBt27YwNMy7enDbtm3g8/kwMjLiOl65R8VIJbTw1EMEXX0p02akLQSPB4xwsoZQLe/qF76qCrrUN6VChBBCvuH169eYNGkSTp48iREjRiAoKAgAYGZmxnGyioOKkUrkzecMBEe9lylEZnapjWGtrKEtpG8FQgiRh1gsRkBAAObMmYO0tDSoqanBwsICjDE6JSMn+gRSQtkiMYbvvIXnH9OlbR9TC94Vd/+YFnCype5DQgiR17179+Dh4YFbt24BAFq1aoWtW7eifv36HCermKgYUQKf03Nw/UUibsV8gkBNBVsvv/jm8gI1FXh3skOLGoZllJAQQpTH8ePH0bdvX4jFYujq6mLlypXw8PCAigpN8FhSVIxUYJeffMSso/fx7qs74H7p7JQ20scqPB5sq2pDlS7DJYSQEmvXrh2MjIzQpk0bbNiwAebm5lxHqvCoGKnA9t+MlSlEeDzAta4JrA21kCtm8OpUC7rqNPiUEEJ+xIcPH7Br1y7MmDEDPB4PVapUwd27d2Fqasp1NKVBxUgFdPLeO6w8+whvkzIB5F0B81uXOtAQVK5bThNCSGlijCEoKAjTp0/H58+fYWFhgUGDBgEAFSIKRsVIBTNu722cexgv0zawuSUVIoQQokCPHz/GuHHjcPHiRQBA48aNYWdnx20oJUbFSAWSmpUrU4jMdbdH/+bV6VQMIYQoSE5ODlauXIklS5YgJycHmpqaWLhwIby8vKCmRh+ZpYWObAVwJ/Yz7r9OwoJTUdK22/M6wpBuSEcIIQrVv39/nDhxAgDQpUsXBAQE0N11ywAVI+XY8Yg32PjvM7xISJdp11VXo0KEEEJKgZeXF65fv45169ZhwIABNHlZGaFipJx6FJ+CqYfuybR1bWCGKlp8LOxBk+oQQsiPYozhzz//RHJyMsaOHQsAcHZ2RkxMDDQ0NDhOV7lQMVJOrTr3WPp4Zpfa6N7QHNUNNDlMRAghyuPVq1eYOHEiTp8+DU1NTXTq1AnW1tYAQIUIB6gYKYdCHn3Af48+AAC61DPFhHa2HCcihBDlIBKJsHHjRvz+++9IT0+HQCDAzJkz6aZ2HKNipJwRSxhG7gqTPp/emS4lI4QQRbhz5w7Gjh2LO3fuAADatGmDLVu2wN7enuNkhIqRckIsYbgZk4jP6bnStpW/NIBtVR0OUxFCiHJISEjATz/9hMzMTOjr62P16tUYNWoU3U+mnKBipJzYFPIMvsFPZNq6N6L7HRBCiCIYGRlh2rRpePbsGdatW0czqJYzVIyUA4wxmUKkqaU+WtsaQVNA/z2EEFIS8fHx8Pb2xrRp0+Dg4AAAWLhwIfWElFP0aVfO7B7VHM52xlzHIISQCkkikWD79u347bffkJSUhGfPnuHmzZvg8XhUiJRjnP/P5M9up66uDgcHB4SGhn5z+X379qFRo0bQ1NSEmZkZRo4cicTExDJKW/rqm+tyHYEQQiqk6OhoODs7w9PTE0lJSXBwcEBgYCBNXFYBcFqMHDp0CF5eXpg7dy4iIiLQpk0buLm5ITY2ttDlr1y5gmHDhmH06NF4+PAh/vzzT4SFhWHMmDFlnJwQQkh5kZWVBR8fHzRq1AhXrlyBlpYW1q1bhxs3bqBp06ZcxyPFwGkx4uvri9GjR2PMmDGwt7fH+vXrUb16dWzevLnQ5W/cuAFra2tMnjwZNjY2+Omnn+Dp6Ynw8PAyTq44yZm5CLj4nOsYhBBSYR0+fBiLFi1Cbm4uunbtiocPH9KN7SoYzoqRnJwc3L59G66urjLtrq6uuHbtWqHrODk54c2bNzhz5gwYY3j//j2OHDmCrl27Frmf7OxspKSkyHyVJ/7/PcXq83mzraqq8CDkq3KciBBCyj/GmPTxkCFD0Lt3bxw+fBinTp2ClZUVh8lISXBWjCQkJEAsFsPExESm3cTEBPHx8YWu4+TkhH379qF///4QCAQwNTWFvr4+Nm7cWOR+li9fDj09PelX9erVFfo+Sur5xzRYzzqNbaEx0ja/AU2gLaRKnhBCisIYw/79+9GyZUukp+fdRFRFRQVHjx5F3759aXxIBcX5ANavv3EYY0V+M0VFRWHy5MmYP38+bt++jXPnziEmJgbjxo0rcvuzZ89GcnKy9Ov169cKzV9SwVHvZZ7vHd0cXRvSdMSEEFKUmJgYuLm5YfDgwbh16xb8/f25jkQUhLM/w42MjKCqqlqgF+TDhw8FekvyLV++HK1bt8aMGTMAAA0bNoSWlhbatGmDJUuWFHpvAaFQCKFQqPg38AOycsVYcfYRAMDORBvnprSFigpV84QQUpjc3FysX78ePj4+yMzMhFAoxLx58zB16lSuoxEF4axnRCAQwMHBAcHBwTLtwcHBcHJyKnSdjIyMAteJq6rmjbH48vxheZaVK0bfwOvS5852xlSIEEJIEcLCwtCsWTPMnDkTmZmZaNeuHe7fv4958+ZBIBBwHY8oCKcDFLy9vTF06FA4OjqiVatW2Lp1K2JjY6WnXWbPno23b99iz549AIDu3btj7Nix2Lx5Mzp37oy4uDh4eXmhefPmMDevGFOnN1p4AdkiifT5zC51OExDCCHl24oVK3Dv3j0YGBhg7dq1GD58OI0LUUKcFiP9+/dHYmIiFi1ahLi4ONSvXx9nzpyRjoSOi4uTmXNkxIgRSE1Nhb+/P6ZNmwZ9fX20b98eK1eu5OotyO3L/pt/vJ3BV+V82A4hhJQrubm54PP5AAA/Pz9UqVIFy5cvh7ExzU6trHisopzfUJCUlBTo6ekhOTkZurplO9vp7Vef8cvmvMuWL81oBytDrTLdPyGElGfv3r3DlClTIBQK8ccff3AdhxSitD5D6c/yMiCRMBy9/UZaiACAmZ4Gh4kIIaT8kEgk2Lx5M+zt7XHkyBEcOnQIz5/TZJCVCU1qUcp8LzyG33/PZNpGtbaBQI3qQEIIefDgATw9PaWTXTZv3hxbt25FzZo1OU5GyhIVI6WAMYZLTz5iRFBYgdeW926AbjSfCCGkksvKysKSJUuwcuVKiEQiaGtrY9myZZgwYYL0KklSeVAxomDp2SL8svkaHsWnyrSfnvwTapvoQI0GrBJCCLKzs7Fz506IRCL07NkTGzduhIWFBdexCEeoGFGwfx99kClEBrWwxILu9ei0DCGk0vv8+TP09fXB4/Ggp6eH7du3Izs7G7169eI6GuEYfUIqWHauWPr41twOWNarARUihJBKjTGGvXv3ws7OTjpvFAC4u7tTIUIAUDGiUG+TMjHjyH0AQLvaxqiqo85xIkII4dbz58/h6uqKYcOGISEhAbt3764wM2aTskPFiAJ57g2XPq5tqsNhEkII4VZubi5WrFiB+vXr459//oG6ujqWLVuG8+fP0wyqpAAaM6JAyZm5AIDqBhqYRdO8E0IqqTt37mDEiBGIjIwEAHTo0AGBgYGwtbXlOBkpr6hnREFyRBK8/pQJAFjfvzFV/oSQSisjIwORkZEwNDTEnj17EBwcTIUI+SbqGVGQbaEvpI+1hHRYCSGVy/Pnz6UTlf3000/YvXs33N3dYWRkxHEyUhFQz8gPEoklGLrjJlaffyxtq21C40UIIZXDmzdv0KtXL9SvX19mCvdhw4ZRIUKKjYqRH8AYw9AdtxD6NEHatnOEI52iIYQoPbFYjI0bN6Ju3bo4ceIERCIRrl69ynUsUkGV6HyCSCTCxYsX8fz5cwwaNAg6Ojp49+4ddHV1oa2treiM5daso5G4/iJR+vzi9HawNqI78RJClNv9+/cxduxY3Lp1CwDQqlUrbN26FfXr1+c4Gamo5C5GXr16hS5duiA2NhbZ2dno1KkTdHR0sGrVKmRlZSEwMLA0cpZL994kSR/fmtuB5hUhhCg9Hx8fLFu2DCKRCLq6ulixYgU8PT2hokId7aTk5P7umTJlChwdHfH582doaGhI23v16oV///1XoeHKuxyxBACwb0wLKkQIIZWCqqoqRCIRevfujaioKIwfP54KEfLD5O4ZuXLlCq5evQqBQCDTbmVlhbdv3yosWHn3+lMGXnxM5zoGIYSUqg8fPuDTp0+oUydv7qTffvsNzZs3R5cuXThORpSJ3OWsRCKBWCwu0P7mzRvo6FSeq0gCLj6TPq5Ds60SQpQMYwxBQUGwt7fHgAEDkJubN6mjUCikQoQonNzFSKdOnbB+/Xrpcx6Ph7S0NPj4+MDd3V2R2cq1my8+AQDsTLRhqC3kOA0hhCjOkydP0KFDB4waNQqfPuX9rnv//j3HqYgyk7sYWbduHS5duoS6desiKysLgwYNgrW1Nd6+fYuVK1eWRsZySchXBQCMb1eT4ySEEKIYOTk5WLJkCRo2bIiQkBBoaGhg1apVCAsLg4WFBdfxiBKTe8yIubk57t69i4MHD+L27duQSCQYPXo0Bg8eLDOgVZnFJKQjOi4FAGCoRb0ihJCKLz4+Hh06dEBUVBQAoHPnzti8eTNsbGw4TkYqA7mLkcuXL8PJyQkjR47EyJEjpe0ikQiXL19G27ZtFRqwPBq286b0cS2TyjOvCiFEeVWtWhWGhoYwNjbGhg0bMGDAAJrAkZQZuU/TuLi4SM8hfik5ORkuLi4KCVXe6WnwAQA9G5vDTK9y9AYRQpQLYwzHjx9HamoqAEBFRQV79+7Fo0ePMHDgQCpESJmSuxhhjBX6TZqYmAgtrcox+2hsYgYAoGeTahwnIYQQ+cXGxqJHjx7o3bs35s+fL223srKCgYEBh8lIZVXs0zS9e/cGkHf1zIgRIyAU/m+shFgsxv379+Hk5KT4hOXMgpMPkZIlAgCo0F8OhJAKJP9+MvPmzUN6ejr4fD6qVKnCdSxCil+M6OnpAcjrGdHR0ZEZrCoQCNCyZUuMHTtW8QnLmTORcdLHTa3oh5gQUjFERETAw8MD4eHhAICffvoJW7duhb29PcfJCJGjGAkKCgIAWFtbY/r06ZXmlMyXXnxMw4fUbADAjuGO0BaW6D6DhBBSpvbv349hw4ZBLBZDT08Pq1evxujRo2kad1JuyP1p6uPjUxo5KoRX/z9WBAAcrem8KiGkYnBxcYG2tja6dOmC9evXw9TUlOtIhMgo0Z/2R44cweHDhxEbG4ucnByZ1+7cuaOQYOVZg2p60itqCCGkvHn//j0OHz6MX3/9FQBgZmaGhw8folo1GnRPyie5++j8/PwwcuRIVK1aFREREWjevDkMDQ3x4sULuLm5lUZGQgghxSCRSLB9+3bUqVMHkydPxpkzZ6SvUSFCyjO5i5GAgABs3boV/v7+EAgEmDlzJoKDgzF58mQkJyeXRkZCCCHfER0djXbt2mHs2LFISkqCg4MDzM3NuY5FSLHIXYzExsZKL+HV0NCQTpgzdOhQHDhwQLHpypm45CyuIxBCiIzs7GwsWLAAjRo1QmhoKLS0tODr64sbN26gcePGXMcjpFjkHjNiamqKxMREWFlZwcrKCjdu3ECjRo0QExMDxlhpZCwXsnLFmHM8EgCQmSvmOA0hhOTp3r07goODAQBdu3bFpk2bYGVlxXEqQuQjd89I+/btcerUKQDA6NGjMXXqVHTq1An9+/dHr169FB6wvFgX/ET6uL9jdQ6TEELI/0yaNAkmJiY4dOgQTp06RYUIqZB4TM7uDIlEAolEAjW1vE6Vw4cP48qVK7C1tcW4ceMgEAhKJaiipKSkQE9PD8nJydDV1S32el39QvHwXd6demOWu9N9GwghZY4xhoMHDwIABg4cKG1PT0+vlHM/kbJX0s/Q75H7NI2KiorMRDn9+vVDv379AABv375V2hHbQrW897yidwMqRAghZS4mJgYTJkzAuXPnoK+vj/bt28PExAQAqBAhFZ5Cpt+Lj4/Hr7/+CltbW0Vsrlwz0CrfPT+EEOUiEomwZs0a1KtXD+fOnYNAIIC3tzf09fW5jkaIwhS7GElKSsLgwYNhbGwMc3Nz+Pn5QSKRYP78+ahRowZu3LiBnTt3lmZWznxOz8Gd2CSuYxBCKpmwsDA0a9YMM2bMQGZmJpydnXH//n38/vvvMjcrJaSiK/Zpmjlz5uDy5csYPnw4zp07h6lTp+LcuXPIysrC2bNn4ezsXJo5OfXg3f/mT6ltqsNhEkJIZfHmzRs4OTlBJBKhSpUqWLNmDUaOHEmniYlSKnYxcvr0aQQFBaFjx46YMGECbG1tYWdnh/Xr15divPLFylATVoZ0bpYQUvosLCwwfvx4fPr0Cb6+vqhatSrXkQgpNcU+TfPu3TvUrVsXAFCjRg2oq6tjzJgxpRasPNLgq3IdgRCipN69e4eBAwfi8ePH0rZ169bhjz/+oEKEKL1i94xIJBLw+f+7OZyqqiqN4CaEkB8kkUiwZcsWzJo1CykpKUhISJBOYqaqSn8Akcqh2MUIYwwjRoyQDprKysrCuHHjChQkx44dU2xCQghRUg8ePICnpyeuXbsGAGjWrBlWr17NcSpCyl6xi5Hhw4fLPB8yZIjCwxBCSGWQlZWFJUuWYOXKlRCJRNDW1sbSpUsxceJE6g0hlVKxi5GgoKDSzFGuvUxI5zoCIUSJbN++HUuXLgUA9OjRA/7+/qhenW4zQSovuWdgrYz233oNAEjLFnGchBBSUTHGpJflenp64u+//4aHhwd69epFl+uSSk8hM7AqO21hXrdpt4bmHCchhFQ0jDH88ccf6NChA3JycgAAfD4f586dQ+/evakQIQRUjMilcXU9riMQQiqQ58+fo3Pnzhg6dChCQkKwfft2riMRUi5xXowEBATAxsYG6urqcHBwQGho6DeXz87Oxty5c2FlZQWhUIiaNWsq7TT0hJCKKTc3FytWrED9+vURHBwMoVCIZcuWVbq5mQgpLk7HjBw6dAheXl4ICAhA69atsWXLFri5uSEqKgqWlpaFrtOvXz+8f/8eO3bsgK2tLT58+ACRiMZyEELKhxs3bsDDwwORkZEAgPbt2yMwMBC1atXiOBkh5VeJekb27t2L1q1bw9zcHK9evQIArF+/Hn/99Zdc2/H19cXo0aMxZswY2NvbY/369ahevTo2b95c6PLnzp3DpUuXcObMGXTs2BHW1tZo3rw5nJycSvI2CCFE4X7//XdERkbC0NAQu3fvxj///EOFCCHfIXcxsnnzZnh7e8Pd3R1JSUkQi8UAAH19fbnuU5OTk4Pbt2/D1dVVpt3V1VU6AdDXTp48CUdHR6xatQrVqlWDnZ0dpk+fjszMzCL3k52djZSUFJkveeSKJQh7+VmudQghlcuXvbObNm3CyJEj8ejRIwwbNowGqBJSDHIXIxs3bsS2bdswd+5cmcl5HB0dpd2SxZGQkACxWAwTExOZdhMTE8THxxe6zosXL3DlyhU8ePAAx48fx/r163HkyBFMnDixyP0sX74cenp60i95r+WPiE2SPjbSplt2E0L+5+3bt+jduze8vLykbXZ2dti5cyeMjIy4C0ZIBSN3MRITE4MmTZoUaBcKhUhPl39ysK//avjyWvyvSSQS8Hg87Nu3D82bN4e7uzt8fX2xa9euIntHZs+ejeTkZOnX69ev5conEkukjx2sqsi1LiFEOYnFYmzatAn29vY4fvw4tm3bhrdv33Idi5AKS+5ixMbGBnfv3i3QfvbsWeldfYvDyMgIqqqqBXpBPnz4UKC3JJ+ZmRmqVasGPb3/XWJrb28PxhjevHlT6DpCoRC6uroyXyVR20SHulsJIbh//z5at26NSZMmITU1FS1btkR4eDiqVavGdTRCKiy5i5EZM2Zg4sSJOHToEBhjuHXrFpYuXYo5c+ZgxowZxd6OQCCAg4OD9O6U+YKDg4sckNq6dWu8e/cOaWlp0rYnT55ARUUFFhYW8r6VYsnMFZfKdgkhFUtmZiZmz54NBwcH3Lx5Ezo6Oti0aROuXLmCBg0acB2PkIqNlcDWrVuZpaUl4/F4jMfjMQsLC7Z9+3a5t3Pw4EHG5/PZjh07WFRUFPPy8mJaWlrs5cuXjDHGZs2axYYOHSpdPjU1lVlYWLA+ffqwhw8fskuXLrFatWqxMWPGFHufycnJDABLTk4u1vJd/S4zq9/+Zi5rQuR6b4QQ5fL+/XtWpUoVBoD17t2bvXnzhutIhJQ5eT9Di6tE84yMHTsWY8eORUJCAiQSCapWrVqiQqh///5ITEzEokWLEBcXh/r16+PMmTOwsrICAMTFxSE2Nla6vLa2NoKDg/Hrr7/C0dERhoaG6NevH5YsWVKi/ReHpiDvENkaa5faPggh5VNycrL0tHDVqlURGBgIgUCAnj17chuMECXDY4wxeVZYuHAhhgwZgpo1a5ZWplKVkpICPT09JCcnF2v8SL8t13Er5hMCBjeFewOzMkhICOEaYwy7d+/GtGnTsHPnTvz8889cRyKkXJD3M7S45B4zcvToUdjZ2aFly5bw9/fHx48fFRaGEEK49vTpU3To0AEjR47Ep0+fsG3bNq4jEaL05C5G7t+/j/v376N9+/bw9fVFtWrV4O7ujv379yMjI6M0MhJCSKnLycnB0qVL0aBBA4SEhEBDQwMrV67E8ePHuY5GiNIr0XTw9erVw7Jly/DixQuEhITAxsYGXl5eMDU1VXQ+Qggpdbdu3ULTpk0xb948ZGdnw9XVFQ8ePMDMmTPB5/O5jkeI0vvhu/ZqaWlBQ0MDAoEAubm5ishECCFlKiEhAQ8fPoSxsTH++OMPnDt3DjVq1OA6FiGVRomKkZiYGCxduhR169aFo6Mj7ty5gwULFhQ5jXtFlp5NdwQmRNkwxvDy5Uvpc3d3dwQGBiI6OhqDBw+mCQ4JKWNyX9rbqlUr3Lp1Cw0aNMDIkSMxaNAgpZ158H1KFh6+k+/GeoSQ8u3169eYOHEiLl26hOjoaJibmwMAPD09OU5GSOUldzHi4uKC7du3o169eqWRp1x58fF/99qh+9IQUrGJxWL4+/tj7ty5SE9PB5/Px9WrV9G3b1+uoxFS6cldjCxbtqw0cpRrtlW1YaKrznUMQkgJRUREwMPDA+Hh4QDybi2xdetWue6nRQgpPcUqRry9vbF48WJoaWnB29v7m8v6+voqJFh58Ck9h+sIhJAfwBjDnDlzsHr1aojFYujp6WHVqlUYM2YMVFR+ePw+IURBilWMRERESK+UiYiIKNVA5cmWy88BAJk5dLM8QioiHo+H7OxsiMVi9OvXD+vXr4eZGc2kTEh5U6xiJCQkpNDHyk5ToAoAaGFjwHESQkhxvX//Hunp6dJLcxctWoROnTrBzc2N42SEkKLI3U85atQopKamFmhPT0/HqFGjFBKqvHGpU7IbARJCyg5jDDt27ECdOnUwbNgwSCQSAHk32KRChJDyTe5iZPfu3cjMzCzQnpmZiT179igkVHkRHVew6CKElD+PHj1Cu3btMGbMGCQlJSEjIwMJCQlcxyKEFFOxr6ZJSUkBYwyMMaSmpkJd/X9Xl4jFYpw5cwZVqypPD8KrxHQkZ+aNk1FVoQmQCCmPsrOzsWLFCixbtgw5OTnQ1NTE4sWLMXnyZKipyX2xICGEI8X+adXX1wePxwOPx4OdnV2B13k8HhYuXKjQcFx6n5Itfdza1ojDJISQwsTGxqJz58549OgRgLxZVDdt2gRra2tugxFC5FbsYiQkJASMMbRv3x5Hjx6FgcH/BnUKBAJYWVlJZzJUJjWMtaCnQTfKIqS8MTc3h7q6OkxMTODn54e+ffvSNO6EVFDFLkacnZ0B5N2XxtLSkn7oCSFlijGGv/76C25ubhAKhVBTU8Phw4dhZGSEKlVohmRCKrJiFSP3799H/fr1oaKiguTkZERGRha5bMOGDRUWjitiCcOJu2+5jkEI+X8vX77EhAkTcPbsWfj4+GDBggUAgFq1anEbjBCiEMUqRho3boz4+HhUrVoVjRs3Bo/HA2OswHI8Hg9iccWfIGzhqYfYfzMWAJCaRXftJYQrIpEI69evh4+PDzIyMiAQCGQGzxNClEOxipGYmBgYGxtLHyu7mIT/3SBvdZ+K39NDSEUUHh4ODw8P6azPbdu2xdatW1G7dm2OkxFCFK1YxYiVlVWhj5Xduv6N0K628lyuTEhFsX37dnh6ekIikaBKlSpYvXo1Ro4cSfeTIURJlWjSs9OnT0ufz5w5E/r6+nBycsKrV68UGo4QUjm1b98eQqEQAwcORHR0NEaPHk2FCCFKTO6f7mXLlkFDQwMAcP36dfj7+2PVqlUwMjLC1KlTFR6QC3RjPELKVlxcHHbs2CF9XqNGDTx69Aj79++HiYkJh8kIIWVB7ikKX79+DVtbWwDAiRMn0KdPH3h4eKB169Zo166dovOVuSfvUxH+6jPXMQipFCQSCbZu3YpZs2YhOTkZtWvXxk8//QQAsLS05DgdIaSsyN0zoq2tjcTERADAhQsX0LFjRwCAurp6ofesqWh+P/FA+tjRiu7WS0hpefjwIdq0aYPx48cjOTkZjo6O0NPT4zoWIYQDcveMdOrUCWPGjEGTJk3w5MkTdO3aFUDeLxZlmIZZna8KAOjRyBzVDTQ5TkOI8snKysLSpUuxcuVK5ObmQltbG0uXLsXEiROhqqrKdTxCCAfkLkY2bdqEefPm4fXr1zh69CgMDQ0BALdv38bAgQMVHpArznbGXEcgROkwxtChQwdcu3YNANCjRw/4+/ujevXqHCcjhHBJ7mJEX18f/v7+BdqV6SZ5hJDSwePxMG7cOMTExMDf3x+9evWiW0sQQuQvRgAgKSkJO3bsQHR0NHg8Huzt7TF69Gg630sIkcEYw4EDB6Cjo4Pu3bsDAIYMGYKePXtCR0eH43SEkPJC7gGs4eHhqFmzJtatW4dPnz4hISEB69atQ82aNXHnzp3SyEgIqYBevHiBLl26YPDgwfD09ERSUhKAvN4RKkQIIV+Su2dk6tSp6NGjB7Zt2wY1tbzVRSIRxowZAy8vL1y+fFnhIQkhFUdubi58fX2xcOFCZGZmQigUYuLEidDUpAHhhJDCyV2MhIeHyxQiAKCmpoaZM2fC0dFRoeEIIRXLrVu3MHbsWNy/fx8A4OLigi1bttDddQkh3yT3aRpdXV3ExsYWaH/9+nWF73rNzBHj0pOPXMcgpEJ68uQJWrVqhfv378PQ0BC7du3Cv//+S4UIIeS75O4Z6d+/P0aPHo01a9bAyckJPB4PV65cwYwZMyr8pb2P36dKH9uZVOzCipCyZmdnh8GDB4PH42Ht2rUwMjLiOhIhpIKQuxhZs2YNeDwehg0bBpFIBADg8/kYP348VqxYofCAXNAWqqGBBV0ZRMi3vH37Fr/99huWL18unSdk586dMqdwCSGkOOT+rSEQCLBhwwYsX74cz58/B2MMtra2SjE47UxkHABAX5PPcRJCyi+xWIzAwEDMnj0bqampyMrKwpEjRwCAChFCSIkUe8xIRkYGJk6ciGrVqqFq1aoYM2YMzMzM0LBhQ6UoRADgcXzeaZqPqdkcJyGkfIqMjMRPP/2ESZMmITU1FS1atMD8+fO5jkUIqeCKXYz4+Phg165d6Nq1KwYMGIDg4GCMHz++NLOVqVyxRDp4dX73uhynIaR8yczMxJw5c9C0aVPcuHEDOjo68Pf3x9WrV9GwYUOu4xFCKrhi96keO3YMO3bswIABAwDkzaLYunVriMVipbi5VX6vCADUNNbmMAkh5c/69euxfPlyAECvXr2wceNGVKtWjeNUhBBlUeyekdevX6NNmzbS582bN4eamhrevXtXKsHKWlxylvRxyxqGHCYhpPyZMmUK2rRpg+PHj+PYsWNUiBBCFKrYxYhYLIZAIJBpU1NTk15RU9Htuf4SAKCjTgPwSOXGGMPu3bvRvXt3iMViAICmpiYuX76Mnj17chuOEKKUiv3JyxjDiBEjIBQKpW1ZWVkYN24ctLS0pG3Hjh1TbMIyIlTLq8va2hlznIQQ7jx9+hTjxo3Df//9BwDYv38/hg4dynEqQoiyK3YxMnz48AJtQ4YMUWiY8qBtLZqoiVQ+OTk5WLNmDRYtWoTs7Gyoq6tjwYIF0jFihBBSmopdjAQFBZVmDkIIR65fvw4PDw88ePAAANCpUyds3rwZNWvW5DgZIaSykPveNMrq7utkriMQUuYYY5gyZQoePHgAIyMj7N27F+fPn6dChBBSpqgYARCXnImEtLyJztRU6JAQ5cYYkw5M5fF42LJlC0aOHIlHjx5hyJAh4PF4HCckhFQ29MkLIDEtR/q4g31VDpMQUrpev36Nn3/+GT4+PtK2Jk2aYOfOnTA0pEvaCSHcoGLkC6a66tDXFHx/QUIqGLFYjA0bNqBu3bo4deoU1q9fj8TERK5jEUIIgHJQjAQEBMDGxgbq6upwcHBAaGhosda7evUq1NTU0Lhx49INSEgFd/fuXbRq1QpeXl5IS0uDk5MTbt68ST0hhJByo0TFyN69e9G6dWuYm5vj1atXAPKmi/7rr7/k2s6hQ4fg5eWFuXPnIiIiAm3atIGbmxtiY2O/uV5ycjKGDRuGDh06lCR+ASci3ipkO4SUJ+np6Zg5cyYcHR0RFhYGPT09BAYGIjQ0FPXq1eM6HiGESMldjGzevBne3t5wd3dHUlKSdCCcvr4+1q9fL9e2fH19MXr0aIwZMwb29vZYv349qlevjs2bN39zPU9PTwwaNAitWrX67j6ys7ORkpIi8/UlsYRh+5UYAEBiOt2tlyiPjx8/YtOmTRCLxejbty+io6Ph6ekJFRqkTQgpZ+T+rbRx40Zs27YNc+fOlblBnqOjIyIjI4u9nZycHNy+fRuurq4y7a6urrh27VqR6wUFBeH58+cyA/C+Zfny5dDT05N+Va9eXeb1GUfuSR+v7de42PkJKY/S0tKkj62treHv749Tp07h8OHDMDMz4zAZIYQUTe5iJCYmBk2aNCnQLhQKkZ6eXuztJCQkQCwWw8TERKbdxMQE8fHxha7z9OlTzJo1C/v27YOaWvHma5s9ezaSk5OlX69fv5Z5PSNbLH3cuZ7J16sTUiEwxrBjxw5YWVnh4sWL0vaRI0eiW7du3AUjhJBikLsYsbGxwd27dwu0nz17FnXr1pU7wNdzGjDGCp3nQCwWY9CgQVi4cCHs7OyKvX2hUAhdXV2Zr8Is6VkfQjXVQl8jpDx7/PgxXFxcMGbMGHz69Om7pzkJIaS8kfsWtTNmzMDEiRORlZUFxhhu3bqFAwcOYPny5di+fXuxt2NkZARVVdUCvSAfPnwo0FsCAKmpqQgPD0dERAQmTZoEAJBIJGCMQU1NDRcuXED79u3lfTuEVFjZ2dlYuXIlli5dipycHGhqamLRokWYMmUK19EIIUQuchcjI0eOhEgkwsyZM5GRkYFBgwahWrVq2LBhg1w31RIIBHBwcEBwcDB69eolbQ8ODsbPP/9cYHldXd0CY1ICAgLw33//4ciRI7CxsZH3rRBSYV27dg1jxoxBdHQ0AMDNzQ0BAQGwtrbmNhghhJSA3MUIAIwdOxZjx45FQkICJBIJqlYt2ayl3t7eGDp0KBwdHdGqVSts3boVsbGxGDduHIC88R5v377Fnj17oKKigvr168usX7VqVairqxdoJ0TZvXr1CtHR0ahatSr8/PzQr18/msadEFJhlagYyWdkZPRDO+/fvz8SExOxaNEixMXFoX79+jhz5gysrKwAAHFxcd+dc4SQyoAxhrdv38LCwgIAMGDAAHz48AHDhg1DlSpVOE5HCCE/hscYY/KsYGNj882/wF68ePHDoUpTSkoK9PT0kJycDF1dXYzeFYZ/H33Akp71MaSlFdfxCCng1atXmDBhAsLDwxEdHQ0DAwOuIxFCKqmvP0MVRe6eES8vL5nnubm5iIiIwLlz5zBjxgxF5SoTKVm5+PfRB65jEFIokUgEPz8//P7778jIyIBAIMDVq1fRvXt3rqMRQohCyV2MFDVSf9OmTQgPD//hQGUlM0eMFssuSJ83s6a/Nkn5cfv2bXh4eODOnTsAgLZt22LLli2oU6cOx8kIIUTxFDYvtJubG44ePaqozZW6d0kZ0sf9HC1Q21SHwzSE5GGMYfr06WjevDnu3LkDfX19bN++HSEhIVSIEEKU1g8NYP3SkSNHKuS5bB11Nazq04jrGIQAyJsE8PPnz5BIJBg4cCDWrVtX6Lw7hBCiTOQuRpo0aSIzgJUxhvj4eHz8+BEBAQEKDVcW1FTockjCrbi4OEgkElSrVg0AsHr1avTt2xddunThOBkhhJQNuYuRnj17yjxXUVGBsbEx2rVrV6G6kZ9+SPv+QoSUIolEgu3bt2PmzJlwcnLC6dOnwePxYGBgQIUIIaRSkasYEYlEsLa2RufOnWFqalpamcrE2QdxAIDPGbkcJyGVUVRUFDw8PHD16lUAebdBSE5Ohr6+PrfBCCGEA3INYFVTU8P48eORnZ1dWnnKDF8l760PbmHJcRJSmWRlZWH+/Plo3Lgxrl69Ci0tLaxfvx43b96kQoQQUmnJfZqmRYsWiIiIkM6SWtHVqqrNdQRSSTx9+hTdunXDkydPAADdunXDpk2bYGlJBTEhpHKTuxiZMGECpk2bhjdv3sDBwQFaWloyrzds2FBh4QhRJhYWFpBIJDAzM4Ofnx9++eUXup8MIYRAjmJk1KhRWL9+Pfr37w8AmDx5svQ1Ho8Hxhh4PB7EYrHiUxJSATHG8Pfff8PNzQ1qamrQ0NDAiRMnUK1aNTolQwghXyj2mJHdu3cjKysLMTExBb5evHgh/ZcQknePpi5duqBHjx7w9/eXtterV48KEUII+Uqxe0by76enLGNF/on+APA1uI5BlExubi7WrVuHBQsWIDMzE0KhEBKJhOtYhBBSrsk1ZkSZzm+LJAwqADQFCpuEllRyYWFhGDt2LO7duwcAcHFxwZYtW1CrVi2OkxFCSPkm1yexnZ3ddwuST58+/VCgsube0IzrCEQJ+Pv7Y/LkyWCMwcDAAL6+vhg2bJhSFfCEEFJa5CpGFi5cCD09vdLKUub0NPjQFlLPCPlxzs7OUFVVxcCBA7F27VoYGxtzHYkQQioMuT6JBwwYgKpVq5ZWFkIqjHfv3uHixYsYNGgQAKBBgwZ4/PgxatSowXEyQgipeIp9NQ11NxOSdz+ZzZs3w97eHsOGDZOODwFAhQghhJSQ3FfTEFJZPXjwAB4eHrh+/TqAvNmI+Xw+x6kIIaTiK3bPiEQioVM0pFLKzMzEnDlz0KRJE1y/fh06OjrYuHEjrl69irp163IdjxBCKrxKPXozOZPu2Eu+TSKRoHXr1oiIiAAA9OrVC35+frCwsOA4GSGEKI9KXYyMam3DdQRSzqmoqGDkyJH48OED/P390bNnT64jEUKI0in2aRplZFGFZmAlshhj2Lt3L0JCQqRtEyZMQFRUFBUihBBSSip1zwghX3r27BnGjRuHf//9FzVq1EBkZCQ0NTWhqqoKXV1druMRQojSqtQ9I4QAefeTWb58ORo0aIB///0X6urqGDt2LF0pQwghZYR6Rkildv36dXh4eODBgwcAgI4dOyIwMBA1a9bkOBkhhFQeVIyQSuvu3bto3bo1GGMwMjKCr68vhgwZQhP8EUJIGaNihFRajRo1Qvfu3VGlShWsWbMGRkZGXEcihJBKicaMkErj9evXGD58OBISEgDk3eLgyJEj2LVrFxUihBDCIeoZIUpPLBYjICAAc+bMQVpaGlRVVbFz504AoEGqhBBSDlAxQpTavXv34OHhgVu3bgEAWrVqBW9vb45TEUII+VKlPk3T1s6Y6wiklGRkZOC3336Dg4MDbt26BV1dXWzevBlXrlxB/fr1uY5HCCHkC5W2Z0RbqArbqtpcxyClZPHixVi1ahUAoE+fPtiwYQPMzc05TkUIIaQwlbpnhCivmTNnolmzZjh58iT+/PNPKkQIIaQcq7Q9I0R5MMawa9cuXLhwAfv37wePx0OVKlVw8+ZNmjOEEEIqAOoZIRXakydP0L59e4waNQoHDx7EX3/9JX2NChFCCKkYqBghFVJOTg4WL16Mhg0b4uLFi9DU1MTq1avRrVs3rqMRQgiRE52mIRXO1atX4eHhgaioKABAly5dEBAQABsbG46TEUIIKQnqGSEVilgsxtixYxEVFYWqVati//79OHPmDBUihBBSgVExQso9xhgkEgkAQFVVFYGBgRg9ejSio6MxcOBAGhtCCCEVHBUjpFx79eoVunfvDl9fX2lb27ZtsX37dhgYGHCYjBBCiKJQMULKJZFIhHXr1qFevXo4ffo0li5dirS0NK5jEUIIKQVUjJBy586dO2jRogW8vb2Rnp6ONm3a4Nq1a9DWphlzCSFEGVExQsqNtLQ0TJs2Dc2aNcOdO3egr6+Pbdu24eLFi7C3t+c6HiGEkFJCl/aSciM2NhYbN26ERCLBgAEDsG7dOpiamnIdixBCSCmjYoRwKiMjA5qamgCAunXrYu3atbC1tYWbmxvHyQghhJQVOk1DOCGRSLB161ZYWlri9u3b0vZff/2VChFCCKlkOC9G8mfOVFdXh4ODA0JDQ4tc9tixY+jUqROMjY2hq6uLVq1a4fz582WYlihCdHQ0nJ2d4enpicTERPj7+3MdiRBCCIc4LUYOHToELy8vzJ07FxEREWjTpg3c3NwQGxtb6PKXL19Gp06dcObMGdy+fRsuLi7o3r07IiIiyjg5KYmsrCz4+PigUaNGuHLlCrS0tLBu3Tps27aN62iEEEI4xGOMMa523qJFCzRt2hSbN2+Wttnb26Nnz55Yvnx5sbZRr1499O/fH/Pnzy/W8ikpKdDT04P9b0cRtaJ3iXIT+YWGhmLs2LF4/PgxAKBr167YtGkTrKysOE5GCCGkuPI/Q5OTk6Grq6uw7XLWM5KTk4Pbt2/D1dVVpt3V1RXXrl0r1jYkEglSU1O/ORNndnY2UlJSZL5I2Xv48CEeP34MU1NTHD58GKdOnaJChBBCCAAOr6ZJSEiAWCyGiYmJTLuJiQni4+OLtY21a9ciPT0d/fr1K3KZ5cuXY+HChQXaOesOqiQYY4iPj4eZmRkAwMPDAykpKfDw8IC+vj634QghhJQrnA9g/fomZ4yxYt347MCBA1iwYAEOHTqEqlWrFrnc7NmzkZycLP16/fo1ACA7V/JjwUmRYmJi4ObmhlatWiE9PR0AoKKigpkzZ1IhQgghpADOihEjIyOoqqoW6AX58OFDgd6Srx06dAijR4/G4cOH0bFjx28uKxQKoaurK/MFAFM71fqxN0AKyM3NxerVq1GvXj2cP38e8fHxuH79OtexCCGElHOcFSMCgQAODg4IDg6WaQ8ODoaTk1OR6x04cAAjRozA/v370bVr19KOSYopLCwMzZo1w8yZM5GZmYl27drh/v373y0WCSGEEE5nYPX29sbQoUPh6OiIVq1aYevWrYiNjcW4ceMA5J1iefv2Lfbs2QMgrxAZNmwYNmzYgJYtW0p7VTQ0NKCnp8fZ+6jMRCIRpk2bho0bN4IxBgMDA6xduxbDhw8v1uk2QgghhNNipH///khMTMSiRYsQFxeH+vXr48yZM9KrLOLi4mTmHNmyZQtEIhEmTpyIiRMnStuHDx+OXbt2lXV8AkBNTQ2vX78GYwxDhgyBr68vjI2NuY5FCCGkAuF0nhEu5F8j7Xf2Ln7t0ojrOBXSu3fvwOfzpUXH27dv8fDhwwKXaRNCCFEuSjfPCKl4JBIJNm/eDHt7e3h5eUnbq1WrRoUIIYSQEqO79pJiefDgATw8PKRXxzx79kzmjruEEEJISVHPCPmmzMxMzJ07F02aNMH169ehra0NPz8/XLt2jQoRQgghCkE9I6RIUVFR+Pnnn/Hs2TMAwM8//4yNGzeievXqHCcjhBCiTKgYIUWysLBARkYGzM3N4e/vj169enEdiRBCiBKiYoRIMcZw7tw5dOnSBTweD7q6ujh16hRsbW0VOmqaEEII+RKNGSEAgOfPn8PV1RXu7u7SSeYAoGnTplSIEEIIKVVUjFRyubm5WL58OerXr49//vkH6urqSE1N5ToWIYSQSoRO01RiN27cgIeHByIjIwEAHTp0QGBgIGxtbTlORgghpDKhnpFKavXq1XByckJkZCQMDQ2xZ88eBAcHUyFCCCGkzFExUkm1bt0aQN59fR49eoShQ4fSje0IIYRwgk7TVBJv3rxBWFiY9PJcJycnREdHo3bt2hwnI4QQUtlRz4iSE4vF2LhxI+rWrYtBgwZJJzADQIUIIYSQcoF6RpTY/fv3MXbsWNy6dQsA0KpVK4jFYo5TEUIIIbKoZ0QJZWRkYNasWXBwcMCtW7egq6uLgIAAXLlyhXpDCCGElDvUM6JkcnNz0axZM0RFRQEAevfuDT8/P1SrVo3jZIQQQkjhqGdEyfD5fAwYMAAWFhb466+/cPToUSpECCGElGtUjFRwjDEEBQVJx4UAwG+//YaoqCj06NGDw2SEEEJI8dBpmgrsyZMnGDduHEJCQtCwYUOEh4eDz+dDIBBAIBBwHY8QQggpFipGKqCcnBysWrUKS5YsQXZ2NjQ0NDBkyBCatIwoJbFYjNzcXK5jEFJp8Pl8qKqqluk+qRipYK5evQoPDw/pANXOnTtj8+bNsLGx4TgZIYqXlpaGN2/egDHGdRRCKg0ejwcLCwtoa2uX2T6pGKlArly5gjZt2gAAjI2NsWHDBgwYMIB6RIhSEovFePPmDTQ1NWFsbEzf54SUAcYYPn78iDdv3qBWrVpl1kNCxUgF4uTkhLZt26JWrVpYtWoVDAwMuI5ESKnJzc0FYwzGxsbQ0NDgOg4hlYaxsTFevnyJ3NzcMitG6Gqaciw2NhajR49GamoqAEBFRQUXLlzA9u3bqRAhlQb1iBBStrj4maOekXIo/34y8+bNQ3p6OnR1dbFu3ToAgFAo5DgdIYQQolhUjJQzERER8PDwQHh4OADgp59+goeHB8epCCGEkNJDp2nKifT0dEyfPh3NmjVDeHg49PT0sHXrVly6dAn29vZcxyOEkDKTk5MDW1tbXL16lesoSufvv/9GkyZNIJFIuI4ig4qRcmLWrFlYu3YtxGIx+vfvj0ePHmHs2LFQUaH/IkIqkhEjRoDH44HH40FNTQ2WlpYYP348Pn/+XGDZa9euwd3dHVWqVIG6ujoaNGgg/T3wtZCQELi7u8PQ0BCampqoW7cupk2bhrdv334zT0REBPr27QsTExOoq6vDzs4OY8eOxZMnTxT2nhVt69atsLKyQuvWrbmOUmoiIyPh7OwMDQ0NVKtWDYsWLfruJex37txBp06doK+vD0NDQ3h4eCAtLa3QZRMTE2FhYQEej4ekpCRpe7du3cDj8bB//35Fvp0fRp905cTcuXPRqFEjnD59GgcPHoSpqSnXkQghJdSlSxfExcXh5cuX2L59O06dOoUJEybILHP8+HE4OzvDwsICISEhePToEaZMmYKlS5diwIABMh9MW7ZsQceOHWFqaoqjR48iKioKgYGBSE5Oxtq1a4vM8ffff6Nly5bIzs7Gvn37EB0djb1790JPTw+///57id9faU9Ct3HjRowZM+aHtlGeJ8pLSUlBp06dYG5ujrCwMGzcuBFr1qyBr69vkeu8e/cOHTt2hK2tLW7evIlz587h4cOHGDFiRKHLjx49Gg0bNiz0tZEjR2Ljxo2KeCuKwyqZ5ORkBoD5nb3LWQaxWMy2bdvGPDw8ZNolEglHiQgpfzIzM1lUVBTLzMxkjOX9fKRn53LyJc/P5vDhw9nPP/8s0+bt7c0MDAykz9PS0pihoSHr3bt3gfVPnjzJALCDBw8yxhh7/fo1EwgEzMvLq9D9ff78udD29PR0ZmRkxHr27PnN9YKCgpienp7Ma8ePH2dffjz4+PiwRo0asR07djAbGxvG4/FYYGAgMzc3Z2KxWGbd7t27s2HDhsm8n6ZNmzKhUMhsbGzYggULWG5ubqGZGGPs9u3bTEVFhSUnJ8u0z5w5k9WqVYtpaGgwGxsbNm/ePJaTk/PNjBKJhCUlJbGxY8cyY2NjpqOjw1xcXNjdu//7/f/s2TPWo0cPVrVqVaalpcUcHR1ZcHBwkfkUISAggOnp6bGsrCxp2/Lly5m5uXmR32tbtmxhVatWlTneERERDAB7+vRpge07Ozuzf//9lwEo8D3y8uVLBoA9f/680H19/bP3pfzP0K//f34UDWAtY9HR0fD09ERoaCgAoF+/fujQoQMAuoSRkG/JzBWj7vzznOw7alFnaApK9uvyxYsXOHfuHPh8vrTtwoULSExMxPTp0wss3717d9jZ2eHAgQPo378//vzzT+Tk5GDmzJmFbl9fX7/Q9vPnzyMhIUHu9Yry7NkzHD58GEePHoWqqiqqVauGyZMnIyQkRPo77PPnzzh//jxOnTolzTBkyBD4+fmhTZs2eP78uXRAvo+PT6H7uXz5Muzs7KCrqyvTrqOjg127dsHc3ByRkZEYO3YsdHR0ZN7f1xkBoGvXrjAwMMCZM2egp6eHLVu2oEOHDnjy5AkMDAyQlpYGd3d3LFmyBOrq6ti9eze6d++Ox48fw9LSstCMoaGhcHNz++bxmjNnDubMmVPoa9evX4ezs7PM1ZGdO3fG7Nmz8fLly0Jn1M7OzoZAIJA5dZ8//86VK1dga2sLAIiKisKiRYtw8+ZNvHjxotD9W1lZoWrVqggNDUWNGjW++T7KChUjZSQ7OxvLly/HsmXLkJubC01NTSxZsgTOzs5cRyOEKNjff/8NbW1tiMViZGVlAYBMF3z+eI2iBqfXqVNHuszTp0+hq6sLMzMzuTI8ffpUui1FyMnJwd69e2FsbCxt69KlC/bv3y8tRv78808YGBhIny9duhSzZs3C8OHDAQA1atTA4sWLMXPmzCKLkZcvX8Lc3LxA+7x586SPra2tMW3aNBw6dEimGPk643///YfIyEh8+PBB+sG/Zs0anDhxAkeOHIGHhwcaNWqERo0aSbexZMkSHD9+HCdPnsSkSZMKzejo6Ii7d+9+83h9ay6o+Ph4WFtby7SZmJhIXyusGGnfvj28vb2xevVqTJkyBenp6dJiJy4uDkDe58zAgQOxevVqWFpaFlmMAEC1atXw8uXLb76HskTFSBm4fPkyPDw88PjxYwB5lfqmTZtgZWXFcTJCKg4NviqiFnXmbN/ycHFxwebNm5GRkYHt27fjyZMn+PXXXwssx4oYsMgYk/aUfvlYHkVtu6SsrKxkChEAGDx4MDw8PBAQEAChUIh9+/ZhwIAB0l6J27dvIywsDEuXLpWuk1+gZWRkQFNTs8B+MjMzoa6uXqD9yJEjWL9+PZ49e4a0tDSIRKICvSdfZ7x9+zbS0tJgaGhYYB/Pnz8HkHcl48KFC/H333/j3bt3EIlEyMzMRGxsbJHHQkNDQ9oTUVJf/5/m/38V9X9dr1497N69G97e3pg9ezZUVVUxefJkmJiYSI/37NmzYW9vjyFDhnx3/xoaGsjIyPih96BIVIyUspycHAwdOhSxsbEwMTGBn58f+vbtS6dkCJETj8cr8amSsqalpSX9sPLz84OLiwsWLlyIxYsXAwDs7OwA5J22dXJyKrD+o0ePULduXemyycnJiIuLk6t3JH8fjx49QqtWrYpcTkVFpUDhUtjgTy0trQJt3bt3h0QiwenTp9GsWTOEhobK9ABJJBIsXLgQvXv3LrBuYQUHABgZGSEyMlKm7caNGxgwYAAWLlyIzp07Q09PDwcPHiwwePfrjBKJBGZmZrh48WKB/eSfppoxYwbOnz+PNWvWwNbWFhoaGujTpw9ycnIKzQf8+GkaU1NTxMfHy7R9+PABwP96SAozaNAgDBo0CO/fv4eWlhZ4PB58fX2lPSn5PUFHjhwB8L8Cx8jICHPnzsXChQul2/r06VOB4pJLFeMnu4L5ssIVCATYtGkTTp06hRUrVqBKlSocpyOElDUfHx+4ublh/PjxMDc3h6urKwwMDLB27doCxcjJkyfx9OlTaeHSp08fzJo1C6tWrZLOxPylpKSkQsd/uLq6wsjICKtWrcLx48eLXM/Y2BipqalIT0+Xfph/7xREPg0NDfTu3Rv79u3Ds2fPYGdnBwcHB+nrTZs2xePHj+XqRWjSpAk2b94s0yN09epVWFlZYe7cudLlXr169d1tNW3aFPHx8VBTUytwWiRfaGgoRowYgV69egHIu1P0905f/OhpmlatWmHOnDnIycmBQCAAkDeOyNzcvMicX8ovWHbu3Al1dXV06tQJAHD06FFkZmZKlwsLC8OoUaMQGhqKmjVrStuzsrLw/PlzNGnS5Lv7KjMKHQ5bAZT21TQvXrxgXbp0Ydu2bSuV7RNSWXxrRH95VtjVNIwx5uDgwCZOnCh9/ueffzJVVVU2duxYdu/ePRYTE8O2b9/OqlSpwvr06SNzVcWmTZsYj8djo0aNYhcvXmQvX75kV65cYR4eHszb27vILCdOnGB8Pp91796dBQcHs5iYGBYWFsZmzJjB+vfvzxhjLDExkWlpabHJkyezp0+fsn379jFzc/NCr6YpzIULF5hQKGS1a9dmixcvlnnt3LlzTE1Njfn4+LAHDx6wqKgodvDgQTZ37twiMyckJDCBQMAiIyNl3oeamho7cOAAe/bsGduwYQMzMDCQuQqosIwSiYT99NNPrFGjRuzcuXMsJiaGXb16lc2dO5eFhYUxxhjr2bMna9y4MYuIiGB3795l3bt3Zzo6OmzKlClFZvxRSUlJzMTEhA0cOJBFRkayY8eOMV1dXbZmzRrpMjdv3mS1a9dmb968kbZt3LiR3b59mz1+/Jj5+/szDQ0NtmHDhiL3ExISUujVNCEhIUxbW5ulp6cXuh4XV9NQMaIgubm5bPXq1UxDQ4MBYKampjKXbRFC5KNsxci+ffuYQCBgsbGx0rbLly+zLl26MD09PSYQCFjdunXZmjVrmEgkKrB+cHAw69y5M6tSpQpTV1dnderUYdOnT2fv3r37Zp6wsDDWu3dvZmxszIRCIbO1tWUeHh4yl4MeP36c2draMnV1ddatWze2devWYhcjIpGImZmZFXmp6Llz55iTkxPT0NBgurq6rHnz5mzr1q3fzDxgwAA2a9YsmbYZM2YwQ0NDpq2tzfr378/WrVv33WKEMcZSUlLYr7/+yszNzRmfz2fVq1dngwcPlv4/xMTEMBcXF6ahocGqV6/O/P39mbOzc6kWI4wxdv/+fdamTRsmFAqZqakpW7BggUwBml9IxMTESNuGDh3KDAwMmEAgYA0bNmR79uz55j6KKkY8PDyYp6dnketxUYzwGFPwKKdyLiUlBXp6evA7exe/dmn0/RWKISwsDB4eHtJuO2dnZ2zZsgW1a9dWyPYJqYyysrIQExMDGxubIscXEOUUGRmJjh074tmzZ9DR0eE6jlL5+PEj6tSpg/Dw8EKv2gG+/bOX/xmanJxcYADxj6AZWH9AamoqvLy80LJlS9y9exdVqlTBjh07EBISQoUIIYSUUIMGDbBq1apydempsoiJiUFAQECRhQhXaADrD3j06BH8/PzAGMPgwYPh6+uLqlWrch2LEEIqvPy5SYhiNW/eHM2bN+c6RgFUjMgpKytL2m3VrFkzLF26FE2bNkXnztzMf0AIIYRUdHSappgkEgkCAwNhZWWFR48eSdtnz55NhQghhBDyA6gYKYaHDx+iTZs2GD9+PD58+AB/f3+uIxFSaVSyMfaEcI6LnzkqRr4hKysLv//+O5o0aYJr165BW1sbfn5+2LBhA9fRCFF6+VNcf2smTEKI4uX/zOX/DJYFGjNShIsXL8LDw0N6s6kePXrA398f1atX5zgZIZWDmpoaNDU18fHjR/D5fJm7lRJCSodEIsHHjx+hqakJNbWyKxGoGCnCjRs38PTpU5ibm2Pjxo3o1asX3U+GkDLE4/FgZmaGmJiYYk39TQhRDBUVFVhaWpbpZx4VI/+PMYaEhATpjYOmTZsGkUiEX3/9FXp6ehynI6RyEggEqFWrFp2qIaQMCQSCMu+JpGIEwPPnzzF+/Hi8ffsWEREREAgE4PP5mDdvHtfRCKn0VFRUaAZWQpQc5ydh82eCU1dXh4ODA0JDQ7+5/KVLl+Dg4AB1dXXUqFEDgYGBJd53bm4uVq5cifr16yM4OBjPnz9HWFhYibdHCCGEEPlxWowcOnQIXl5emDt3LiIiItCmTRu4ubkhNja20OVjYmLg7u6ONm3aICIiAnPmzMHkyZNx9OhRuff98lEkHB0dMWvWLGRlZaFDhw548OABWrdu/aNvixBCCCFy4PRGeS1atEDTpk2xefNmaZu9vT169uyJ5cuXF1j+t99+w8mTJxEdHS1tGzduHO7du4fr168Xa5/5N/nJZ2hoCF9fXwwdOpQGqBJCCCHfUFo3yuNszEhOTg5u376NWbNmybS7urri2rVrha5z/fp1uLq6yrR17twZO3bsQG5uLvh8foF1srOzkZ2dLX2enJwsfTxgwAAsW7YMhoaGSE1N/ZG3QwghhCi9lJQUAIqfGI2zYiQhIQFisRgmJiYy7SYmJoiPjy90nfj4+EKXF4lESEhIgJmZWYF1li9fjoULFxa6vYMHD+LgwYMlfAeEEEJI5ZSYmKjQK005v5rm61MjjLFvni4pbPnC2vPNnj0b3t7e0udJSUmwsrJCbGwsXbJbRlJSUlC9enW8fv1aod16pGh0zMseHfOyR8e87CUnJ8PS0hIGBgYK3S5nxYiRkRFUVVUL9IJ8+PChQO9HPlNT00KXV1NTg6GhYaHrCIVCCIXCAu16enr0zVvGdHV16ZiXMTrmZY+OedmjY172FD0PCWdX0wgEAjg4OCA4OFimPTg4GE5OToWu06pVqwLLX7hwAY6OjoWOFyGEEEJI+cfppb3e3t7Yvn07du7ciejoaEydOhWxsbEYN24cgLxTLMOGDZMuP27cOLx69Qre3t6Ijo7Gzp07sWPHDkyfPp2rt0AIIYSQH8TpmJH+/fsjMTERixYtQlxcHOrXr48zZ87AysoKABAXFycz54iNjQ3OnDmDqVOnYtOmTTA3N4efnx9++eWXYu9TKBTCx8en0FM3pHTQMS97dMzLHh3zskfHvOyV1jHndJ4RQgghhBDOp4MnhBBCSOVGxQghhBBCOEXFCCGEEEI4RcUIIYQQQjillMVIQEAAbGxsoK6uDgcHB4SGhn5z+UuXLsHBwQHq6uqoUaMGAgMDyyip8pDnmB87dgydOnWCsbExdHV10apVK5w/f74M0yoHeb/P8129ehVqampo3Lhx6QZUQvIe8+zsbMydOxdWVlYQCoWoWbMmdu7cWUZplYO8x3zfvn1o1KgRNDU1YWZmhpEjRyIxMbGM0lZ8ly9fRvfu3WFubg4ej4cTJ058dx2FfIYyJXPw4EHG5/PZtm3bWFRUFJsyZQrT0tJir169KnT5Fy9eME1NTTZlyhQWFRXFtm3bxvh8Pjty5EgZJ6+45D3mU6ZMYStXrmS3bt1iT548YbNnz2Z8Pp/duXOnjJNXXPIe83xJSUmsRo0azNXVlTVq1KhswiqJkhzzHj16sBYtWrDg4GAWExPDbt68ya5evVqGqSs2eY95aGgoU1FRYRs2bGAvXrxgoaGhrF69eqxnz55lnLziOnPmDJs7dy47evQoA8COHz/+zeUV9RmqdMVI8+bN2bhx42Ta6tSpw2bNmlXo8jNnzmR16tSRafP09GQtW7YstYzKRt5jXpi6deuyhQsXKjqa0irpMe/fvz+bN28e8/HxoWJETvIe87NnzzI9PT2WmJhYFvGUkrzHfPXq1axGjRoybX5+fszCwqLUMiqz4hQjivoMVarTNDk5Obh9+zZcXV1l2l1dXXHt2rVC17l+/XqB5Tt37ozw8HDk5uaWWlZlUZJj/jWJRILU1FSF33hJWZX0mAcFBeH58+fw8fEp7YhKpyTH/OTJk3B0dMSqVatQrVo12NnZYfr06cjMzCyLyBVeSY65k5MT3rx5gzNnzoAxhvfv3+PIkSPo2rVrWUSulBT1Gcr5XXsVKSEhAWKxuMCN9kxMTArcYC9ffHx8ocuLRCIkJCTAzMys1PIqg5Ic86+tXbsW6enp6NevX2lEVDolOeZPnz7FrFmzEBoaCjU1pfqxLxMlOeYvXrzAlStXoK6ujuPHjyMhIQETJkzAp0+faNxIMZTkmDs5OWHfvn3o378/srKyIBKJ0KNHD2zcuLEsIldKivoMVaqekXw8Hk/mOWOsQNv3li+snRRN3mOe78CBA1iwYAEOHTqEqlWrllY8pVTcYy4WizFo0CAsXLgQdnZ2ZRVPKcnzfS6RSMDj8bBv3z40b94c7u7u8PX1xa5du6h3RA7yHPOoqChMnjwZ8+fPx+3bt3Hu3DnExMRI73dGSociPkOV6k8kIyMjqKqqFqiaP3z4UKByy2dqalro8mpqajA0NCy1rMqiJMc836FDhzB69Gj8+eef6NixY2nGVCryHvPU1FSEh4cjIiICkyZNApD3QckYg5qaGi5cuID27duXSfaKqiTf52ZmZqhWrRr09PSkbfb29mCM4c2bN6hVq1apZq7oSnLMly9fjtatW2PGjBkAgIYNG0JLSwtt2rTBkiVLqKe7FCjqM1SpekYEAgEcHBwQHBws0x4cHAwnJ6dC12nVqlWB5S9cuABHR0fw+fxSy6osSnLMgbwekREjRmD//v10PldO8h5zXV1dREZG4u7du9KvcePGoXbt2rh79y5atGhRVtErrJJ8n7du3Rrv3r1DWlqatO3JkydQUVGBhYVFqeZVBiU55hkZGVBRkf1YU1VVBfC/v9aJYinsM1Su4a4VQP6lYDt27GBRUVHMy8uLaWlpsZcvXzLGGJs1axYbOnSodPn8y5KmTp3KoqKi2I4dO+jSXjnJe8z379/P1NTU2KZNm1hcXJz0Kykpiau3UOHIe8y/RlfTyE/eY56amsosLCxYnz592MOHD9mlS5dYrVq12JgxY7h6CxWOvMc8KCiIqampsYCAAPb8+XN25coV5ujoyJo3b87VW6hwUlNTWUREBIuIiGAAmK+vL4uIiJBeTl1an6FKV4wwxtimTZuYlZUVEwgErGnTpuzSpUvS14YPH86cnZ1llr948SJr0qQJEwgEzNramm3evLmME1d88hxzZ2dnBqDA1/Dhw8s+eAUm7/f5l6gYKRl5j3l0dDTr2LEj09DQYBYWFszb25tlZGSUceqKTd5j7ufnx+rWrcs0NDSYmZkZGzx4MHvz5k0Zp664QkJCvvn7ubQ+Q3mMUd8VIYQQQrijVGNGCCGEEFLxUDFCCCGEEE5RMUIIIYQQTlExQgghhBBOUTFCCCGEEE5RMUIIIYQQTlExQgghhBBOUTFCCCGEEE5RMUKIktm1axf09fW5jlFi1tbWWL9+/TeXWbBgARo3blwmeQghpY+KEULKoREjRoDH4xX4evbsGdfRsGvXLplMZmZm6NevH2JiYhSy/bCwMHh4eEif83g8nDhxQmaZ6dOn499//1XI/ory9fs0MTFB9+7d8fDhQ7m3U5GLQ0LKAhUjhJRTXbp0QVxcnMyXjY0N17EA5N0JOC4uDu/evcP+/ftx9+5d9OjRA2Kx+Ie3bWxsDE1NzW8uo62tLdftyUvqy/d5+vRppKeno2vXrsjJySn1fRNSmVAxQkg5JRQKYWpqKvOlqqoKX19fNGjQAFpaWqhevTomTJggc5v6r927dw8uLi7Q0dGBrq4uHBwcEB4eLn392rVraNu2LTQ0NFC9enVMnjwZ6enp38zG4/FgamoKMzMzuLi4wMfHBw8ePJD23GzevBk1a9aEQCBA7dq1sXfvXpn1FyxYAEtLSwiFQpibm2Py5MnS1748TWNtbQ0A6NWrF3g8nvT5l6dpzp8/D3V1dSQlJcnsY/LkyXB2dlbY+3R0dMTUqVPx6tUrPH78WLrMt/4/Ll68iJEjRyI5OVnaw7JgwQIAQE5ODmbOnIlq1apBS0sLLVq0wMWLF7+ZhxBlRcUIIRWMiooK/Pz88ODBA+zevRv//fcfZs6cWeTygwcPhoWFBcLCwnD79m3MmjULfD4fABAZGYnOnTujd+/euH//Pg4dOoQrV65g0qRJcmXS0NAAAOTm5uL48eOYMmUKpk2bhgcPHsDT0xMjR45ESEgIAODIkSNYt24dtmzZgqdPn+LEiRNo0KBBodsNCwsDAAQFBSEuLk76/EsdO3aEvr4+jh49Km0Ti8U4fPgwBg8erLD3mZSUhP379wOA9PgB3/7/cHJywvr166U9LHFxcZg+fToAYOTIkbh69SoOHjyI+/fvo2/fvujSpQuePn1a7EyEKI0fvt8wIUThhg8fzlRVVZmWlpb0q0+fPoUue/jwYWZoaCh9HhQUxPT09KTPdXR02K5duwpdd+jQoczDw0OmLTQ0lKmoqLDMzMxC1/l6+69fv2YtW7ZkFhYWLDs7mzk5ObGxY8fKrNO3b1/m7u7OGGNs7dq1zM7OjuXk5BS6fSsrK7Zu3TrpcwDs+PHjMsv4+PiwRo0aSZ9PnjyZtW/fXvr8/PnzTCAQsE+fPv3Q+wTAtLS0mKampvRW6j169Ch0+Xzf+/9gjLFnz54xHo/H3r59K9PeoUMHNnv27G9unxBlpMZtKUQIKYqLiws2b94sfa6lpQUACAkJwbJlyxAVFYWUlBSIRCJkZWUhPT1dusyXvL29MWbMGOzduxcdO3ZE3759UbNmTQDA7du38ezZM+zbt0+6PGMMEokEMTExsLe3LzRbcnIytLW1wRhDRkYGmjZtimPHjkEgECA6OlpmACoAtG7dGhs2bAAA9O3bF+vXr0eNGjXQpUsXuLu7o3v37lBTK/mvo8GDB6NVq1Z49+4dzM3NsW/fPri7u6NKlSo/9D51dHRw584diEQiXLp0CatXr0ZgYKDMMvL+fwDAnTt3wBiDnZ2dTHt2dnaZjIUhpLyhYoSQckpLSwu2trYyba9evYK7uzvGjRuHxYsXw8DAAFeuXMHo0aORm5tb6HYWLFiAQYMG4fTp0zh79ix8fHxw8OBB9OrVCxKJBJ6enjJjNvJZWloWmS3/Q1pFRQUmJiYFPnR5PJ7Mc8aYtK169ep4/PgxgoOD8c8//2DChAlYvXo1Ll26JHP6Qx7NmzdHzZo1cfDgQYwfPx7Hjx9HUFCQ9PWSvk8VFRXp/0GdOnUQHx+P/v374/LlywBK9v+Rn0dVVRW3b9+GqqqqzGva2tpyvXdClAEVI4RUIOHh4RCJRFi7di1UVPKGfB0+fPi769nZ2cHOzg5Tp07FwIEDERQUhF69eqFp06Z4+PBhgaLne778kP6avb09rly5gmHDhknbrl27JtP7oKGhgR49eqBHjx6YOHEi6tSpg8jISDRt2rTA9vh8frGu0hk0aBD27dsHCwsLqKiooGvXrtLXSvo+vzZ16lT4+vri+PHj6NWrV7H+PwQCQYH8TZo0gVgsxocPH9CmTZsfykSIMqABrIRUIDVr1oRIJMLGjRvx4sUL7N27t8Bpgy9lZmZi0qRJuHjxIl69eoWrV68iLCxMWhj89ttvuH79OiZOnIi7d+/i6dOnOHnyJH799dcSZ5wxYwZ27dqFwMBAPH36FL6+vjh27Jh04OauXbuwY8cOPHjwQPoeNDQ0YGVlVej2rK2t8e+//yI+Ph6fP38ucr+DBw/GnTt3sHTpUvTp0wfq6urS1xT1PnV1dTFmzBj4+PiAMVas/w9ra2ukpaXh33//RUJCAjIyMmBnZ4fBgwdj2LBhOHbsGGJiYhAWFoaVK1fizJkzcmUiRClwOWCFEFK44cOHs59//rnQ13x9fZmZmRnT0NBgnTt3Znv27GEA2OfPnxljsgMms7Oz2YABA1j16tWZQCBg5ubmbNKkSTKDNm/dusU6derEtLW1mZaWFmvYsCFbunRpkdkKG5D5tYCAAFajRg3G5/OZnZ0d27Nnj/S148ePsxYtWjBdXV2mpaXFWrZsyf755x/p618PYD158iSztbVlampqzMrKijFWcABrvmbNmjEA7L///ivwmqLe56tXr5iamho7dOgQY+z7/x+MMTZu3DhmaGjIADAfHx/GGGM5OTls/vz5zNramvH5fGZqasp69erF7t+/X2QmQpQVjzHGuC2HCCGEEFKZ0WkaQgghhHCKihFCCCGEcIqKEUIIIYRwiooRQgghhHCKihFCCCGEcIqKEUIIIYRwiooRQgghhHCKihFCCCGEcIqKEUIIIYRwiooRQgghhHCKihFCCCGEcOr/AEJ8VthrecP2AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Basic evaluation metrics\n", + "# Print accuracy\n", + "print(\"Accuracy:\", round(accuracy_score(y_test, y_pred) * 100, 4))\n", + "\n", + "# score the model accuracy scores and store in variables for display later\n", + "acc_gbc_train = gbm.score(X_train, y_train)\n", + "acc_gbc_test = gbm.score(X_test, y_test)\n", + "\n", + "# scores\n", + "print('Train Accuracy:', round(acc_gbc_train * 100, 4))\n", + "print('Test Accuracy:', round(acc_gbc_test * 100, 4))\n", + "\n", + "# Classification report\n", + "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))\n", + "\n", + "#Cross Validation\n", + "# Perform 5-fold cross-validation\n", + "scores = cross_val_score(gbm, X_train, y_train, cv=5, scoring='accuracy').round(4) * 100\n", + "print(\"Cross-validation scores:\", scores)\n", + "print(\"Mean cross-validation score:\", scores.mean().round(4))\n", + "precision_scores = cross_val_score(gbm, X_train, y_train, cv=5, scoring='precision').round(4) * 100\n", + "print(\"Precision scores:\", precision_scores.mean().round(4))\n", + "recall_scores = cross_val_score(gbm, X_train, y_train, cv=5, scoring='recall').round(4) * 100\n", + "print(\"Recall scores:\", recall_scores.mean().round(4))\n", + "f1_scores = cross_val_score(gbm, X_train, y_train, cv=5, scoring='f1').round(4) * 100\n", + "print(\"F1 scores:\", f1_scores.mean().round(4))\n", + "\n", + "# Confusion Matrix\n", + "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n", + "conf_mat = confusion_matrix(y_test, y_pred)\n", + "plt.figure(figsize=(3, 2))\n", + "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues')\n", + "plt.xlabel('Predicted Labels')\n", + "plt.ylabel('Actual Labels')\n", + "plt.title('Confusion Matrix')\n", + "plt.show()\n", + "\n", + "# ROC Curve and AUC\n", + "print(\"ROC-AUC Score:\", roc_auc_score(y_test, gbm.predict_proba(X_test)[:, 1]))\n", + "\n", + "y_prob = gbm.predict_proba(X_test)[:, 1]\n", + "fpr, tpr, thresholds = roc_curve(y_test, y_prob)\n", + "roc_auc = auc(fpr, tpr)\n", + "\n", + "plt.figure(figsize=(6, 4))\n", + "plt.plot(fpr, tpr, label='ROC Curve (area = %0.2f)' % roc_auc)\n", + "plt.plot([0, 1], [0, 1], 'k--')\n", + "plt.xlim([0.0, 1.0])\n", + "plt.ylim([0.0, 1.05])\n", + "plt.xlabel('False Positive Rate')\n", + "plt.ylabel('True Positive Rate')\n", + "plt.title('Receiver Operating Characteristic')\n", + "plt.legend(loc=\"lower right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7Ucg0ixwH3ok", + "metadata": { + "id": "7Ucg0ixwH3ok" + }, + "source": [ + "The 87.458% accuracy is great and the .94 ROC area as well.\n", + "\n", + "\n", + "\n", + "#### Model Evaluation Metrics Table\n", + "***Model Added: 11. Gradient Boosting Classifier***\n", + "\n", + "| Number | Model | Accuracy | Training | Precision-0 | Precision-1 | Recall-0 | Recall-1 | F1-0 | F1-1 | Mean | Precision | Recall | F1 | ROC |\n", + "|--------|-------|----------|----------|-------------|-------------|-----------|-----------|-------|-------|-------|-----------|--------|------|-----|\n", + "| 1 | Baseline - Logistic Regression Model with Robust scaled 25% test data set | 86.8842 | 87.611 | 86 | 88 | 89 | 85 | 87 | 86 | 86.39 | 88.142 | 84.376 | 86.218 | 0.93|\n", + "| 2 | Baseline with 98% PCA | 86.6178 | 86.5897 | 85 | 88 | 89 | 84 | 87 | 87 | 86.124 | 88.006 | 83.936 | 85.916 | 0.92|\n", + "| 3 | Baseline with 95% PCA | 86.0693 | 87.8553 | 87 | 84 | 93 | 72 | 90 | 77 | 87.37 | 87.692 | 70.002 | 77.83 | 0.92|\n", + "| 4 | Baseline with 93% PCA | 85.8189 | 85.857 | 85 | 87 | 88 | 83 | 87 | 86 | 85.236 | 88.052 | 81.868 | 84.836 | 0.92 |\n", + "| 5 | KNN k=5 with 98% PCA k=5 | 79.9601 | 87.944 | 82 | 78 | 78 | 82 | 80 | 80 | 79.372 | 80.856 | 77.462 | 79.106 | 0.89|\n", + "| 6 | KNN k=5 with 95% PCA k=5 | 79.9601 | 88.2327 | 81 | 79 | 79 | 81 | 80 | 80 | 79.308 | 80.99 | 77.066 | 78.974 | 0.89|\n", + "| 7 | KNN k=5 with 93% PCA k=5 | 79.8269 | 87.9218 | 82 | 78 | 78 | 82 | 80 | 80 | 79.218 | 80.88 | 77.022 | 78.896 | 0.88|\n", + "| 8 | KNN k=1 with 98% PCA k=5 | 89.281 | 100.0 | 93 | 86 | 85 | 94 | 89 | 89 | 77.886 | 84.702 | 68.616 | 75.798 | 0.87 |\n", + "| 9 | Random Forest estimator=100 | 93.8402 | 100.0 | 95 | 92 | 93 | 95 | 94 | 94 | 92.96 | 92.306 | 94.02 | 93.15 | 0.98 |\n", + "| 10 | Decision Tree | 89.3452 | 100.0 | 94 | 85 | 85 | 94 | 89 | 89 | 86.988 | 84.126 | 91.822 | 87.79 | 0.90|\n", + "| 11 | Gradient Boosting Classifier | 87.4584 | 89.7003 | 87 | 88 | 89 | 86 | 87 | 87 | 87.512 | 89.35 | 85.702 | 87.482 | 0.94\n", + "\n", + "\n", + "
\n", + "
\n", + "\n", + "Let's do hyperparameter on this." + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "id": "iiloJSJYH84N", + "metadata": { + "id": "iiloJSJYH84N" + }, + "outputs": [], + "source": [ + "param_grid = {\n", + " 'n_estimators': [100, 200, 300],\n", + " 'learning_rate': [0.01, 0.1, 0.2],\n", + " 'max_depth': [3, 5, 7],\n", + " 'min_samples_split': [2, 5],\n", + " 'min_samples_leaf': [1, 2]\n", + "}\n", + "\n", + "grid_search = GridSearchCV(GradientBoostingClassifier(random_state=42), param_grid, cv=5, scoring='roc_auc', verbose=1)\n", + "# grid_search.fit(X_train, y_train)\n", + "\n", + "# # # Best parameters and best score\n", + "# print(\"Best parameters:\", grid_search.best_params_)\n", + "# print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_))\n" + ] + }, + { + "cell_type": "markdown", + "id": "0RQl3l1hIICs", + "metadata": { + "id": "0RQl3l1hIICs" + }, + "source": [ + "The code block above for GBC hyperparameter tunning was taking too long to execute and had to interupt the code mid way.\n", + "\n", + "I used Google Colab to run it and got the results below.\n", + "\n", + "Fitting 5 folds for each of 108 candidates, totalling 540 fits\n", + "\n", + " Best parameters: {'learning_rate': 0.2, 'max_depth': 7, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 300}\n", + "\n", + " Best cross-validation score: 0.97\n", + "\n", + "\n", + "Let's move on and do learning and validation curve instead.\n", + "\n", + "#### Learning Curve for Gradient Boosting Classifier\n", + "\n", + "Reusing the function let's pass in the training data with no scaling or PCA." + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "id": "_Dli_dB3IQ_u", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "_Dli_dB3IQ_u", + "outputId": "d64a8803-f955-45d7-cbcd-406a14468c80" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gradient_boosting = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)\n", + "plot_learning_curve(gradient_boosting, \"Learning Curve for Gradient Boosting\", X_train, y_train, cv=5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "WqMQ69jiIVQ8", + "metadata": { + "id": "WqMQ69jiIVQ8" + }, + "source": [ + "This Gradient Boosting model learning plot is typical and it should get better at generalizing with more data but tends to overfit when trained on smaller datasets. To optimize further, adjust the model's settings to reduce overfitting (such as the hyperparameters)." + ] + }, + { + "cell_type": "markdown", + "id": "bhfuUTYhIXEt", + "metadata": { + "id": "bhfuUTYhIXEt" + }, + "source": [ + "#### Validation Curve for Gradient Boosting Classifier\n", + "\n", + "Same here with reusing the validation curve plot function and passing in regular training data." + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "id": "TqourPdbIU0s", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 569 + }, + "id": "TqourPdbIU0s", + "outputId": "7d99bf79-90a4-49f8-cd2f-1dc3f0cd5c63" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "\n", + "# Gradient Boosting model\n", + "model_gb = GradientBoostingClassifier(random_state=42)\n", + "param_name_gb = 'n_estimators'\n", + "param_range_gb = np.arange(10, 201, 10) # Checking values from 10 to 200\n", + "\n", + "# Plotting the validation curve\n", + "plot_validation_curve(model_gb, \"Validation Curve for Gradient Boosting\", X_train, y_train,\n", + " param_name=param_name_gb, param_range=param_range_gb, cv=5)" + ] + }, + { + "cell_type": "markdown", + "id": "10r3fKyZIgxb", + "metadata": { + "id": "10r3fKyZIgxb" + }, + "source": [ + "This validation curve on gradient boosting shows that both training and cross-validation scores are stable and very close across the range of estimator values. No further tuning is needed." + ] + }, + { + "cell_type": "markdown", + "id": "vGW2KsIgIjvP", + "metadata": { + "id": "vGW2KsIgIjvP" + }, + "source": [ + "# **Summary**\n", + "\n", + "#### Model Evaluation Metrics Table In Accuracy Descending Order\n", + "\n", + "| Number | Model | Accuracy | Training | Precision-0 | Precision-1 | Recall-0 | Recall-1 | F1-0 | F1-1 | Mean | Precision | Recall | F1 | ROC |\n", + "|--------|-------|----------|----------|-------------|-------------|-----------|-----------|-------|-------|-------|-----------|--------|------|-----|\n", + "| 9 | Random Forest estimator=100 | 93.84 | 100.0 | 95 | 92 | 93 | 95 | 94 | 94 | 92.96 | 92.306 | 94.02 | 93.15 | 0.98 |\n", + "| 10 | Decision Tree | 89.34 | 100.0 | 94 | 85 | 85 | 94 | 89 | 89 | 86.988 | 84.126 | 91.822 | 87.79 | 0.90|\n", + "| 8 | KNN k=1 with 98% PCA k=5 | 89.28 | 100.0 | 93 | 86 | 85 | 94 | 89 | 89 | 77.886 | 84.702 | 68.616 | 75.798 | 0.87 |\n", + "| 11 | Gradient Boosting Classifier | 87.45 | 89.70 | 87 | 88 | 89 | 86 | 87 | 87 | 87.512 | 89.35 | 85.702 | 87.482 | 0.94|\n", + "| 1 | Baseline - Logistic Regression Model with Robust scaled 25% test data set | 86.88 | 87.61 | 86 | 88 | 89 | 85 | 87 | 86 | 86.39 | 88.142 | 84.376 | 86.218 | 0.93|\n", + "| 2 | Baseline with 98% PCA | 86.61 | 86.58 | 85 | 88 | 89 | 84 | 87 | 87 | 86.124 | 88.006 | 83.936 | 85.916 | 0.92|\n", + "| 3 | Baseline with 95% PCA | 86.06 | 87.85 | 87 | 84 | 93 | 72 | 90 | 77 | 87.37 | 87.692 | 70.002 | 77.83 | 0.92|\n", + "| 4 | Baseline with 93% PCA | 85.81 | 85.85 | 85 | 87 | 88 | 83 | 87 | 86 | 85.236 | 88.052 | 81.868 | 84.836 | 0.92 |\n", + "| 5 | KNN k=5 with 98% PCA k=5 | 79.96 | 87.94 | 82 | 78 | 78 | 82 | 80 | 80 | 79.372 | 80.856 | 77.462 | 79.106 | 0.89|\n", + "| 6 | KNN k=5 with 95% PCA k=5 | 79.96 | 88.23 | 81 | 79 | 79 | 81 | 80 | 80 | 79.308 | 80.99 | 77.066 | 78.974 | 0.89|\n", + "| 7 | KNN k=5 with 93% PCA k=5 | 79.82 | 87.92 | 82 | 78 | 78 | 82 | 80 | 80 | 79.218 | 80.88 | 77.022 | 78.896 | 0.88|" + ] + }, + { + "cell_type": "markdown", + "id": "gLPcssceIxrP", + "metadata": { + "id": "gLPcssceIxrP" + }, + "source": [ + "From this process we were able to determine a model that gives the best possible accuracy.\n", + "\n", + "We processed the data by upsampling the minority class due to the imbalance of the target variable and binning some of the high cardinality categorical features either domain knowledge grouping or frequency grouping.\n", + "\n", + "We ran the data through a number of models and tweaked each of those models to see how the accuracy varies. We learned that the sizing of the splitting of the test vs training data makes a difference in the outcome. PCA reduction also makes a different sometimes not for the better. We also learned that the amount that the PCA reduces in features also depends on the type of scaling done on the data. From trying out 3 different scalers, Robust was the best performer for this data compared to Standard and MinMax.\n", + "\n", + "When we started with the base logistic regression model, we find it to performed well and that adjusting the diffferent PCA components varied in both direction, decreasing more sometimes performs better but there is a diminishing return where it continues to decline in performance.\n", + "\n", + "Moving onto KNN models thoough didn't improve in results until we tried to find the ideal k neighbors value. Once we found that we received a new high scorer with KNN k=1 at 89.28% accuracy.\n", + "\n", + "When Random Forest was attempted we found that to be a big jump in performance. Hyperparameter tunning didn't give any better results though.\n", + "\n", + "Decision Tree came in second in scoring and Gradient Boosting scored below KNN's k=1 best score.\n", + "\n", + "Random Forest's 93.84% accuracy is excellent when it comes to determining the likelyhood of student withdrawal. As a future project perhaps further tune this particular model." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..3c96774 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +streamlit>=1.28.0 +pandas>=1.5.0 +numpy>=1.24.0 +scikit-learn>=1.3.0 +plotly>=5.15.0 +pickle5>=0.0.11 \ No newline at end of file diff --git a/streamlit_app.py b/streamlit_app.py new file mode 100644 index 0000000..5e0af2a --- /dev/null +++ b/streamlit_app.py @@ -0,0 +1,547 @@ +""" +Student Success Prediction Dashboard +A beautiful Streamlit app for predicting student academic outcomes using the trained Random Forest model. +""" + +import streamlit as st +import pandas as pd +import numpy as np +import pickle +import plotly.express as px +import plotly.graph_objects as go +from plotly.subplots import make_subplots + +# Configure Streamlit page +st.set_page_config( + page_title="๐ŸŽ“ Student Success Predictor", + page_icon="๐ŸŽ“", + layout="wide", + initial_sidebar_state="expanded" +) + +# Custom CSS for beautiful styling +st.markdown(""" + +""", unsafe_allow_html=True) + +@st.cache_data +def load_model_and_info(): + """Load the trained model and preprocessing information""" + try: + with open('random_forest_model.pkl', 'rb') as f: + model = pickle.load(f) + + with open('preprocessing_info.pkl', 'rb') as f: + preprocessing_info = pickle.load(f) + + with open('feature_names.pkl', 'rb') as f: + feature_names = pickle.load(f) + + return model, preprocessing_info, feature_names + except FileNotFoundError: + st.error("โŒ Model files not found! Please run 'train_and_save_model.py' first.") + st.stop() + +def create_feature_mappings(): + """Create human-readable mappings for categorical features""" + + # Marital status mapping + marital_status_map = { + 1: "Single", + 2: "Married", + 3: "Widowed", + 4: "Divorced", + 5: "Civil Union", + 6: "Legally Separated" + } + + # Application mode mapping (simplified for demo) + application_mode_map = { + 1: "1st Phase - General Contingent", + 2: "Ordinance No. 612/93", + 5: "1st Phase - Special Contingent (Azores)", + 7: "Holders of Other Higher Courses", + 10: "Ordinance No. 854-B/99", + 15: "International Student (Bachelor)", + 16: "1st Phase - Special Contingent (Madeira)", + 17: "2nd Phase - General Contingent", + 18: "3rd Phase - General Contingent", + 26: "Ordinance No. 533-A/99, item b2", + 27: "Ordinance No. 533-A/99, item b3", + 39: "Over 23 years old", + 42: "Transfer", + 43: "Change of course", + 44: "Technological specialization diploma holders", + 51: "Change of institution/course", + 53: "Short cycle diploma holders", + 57: "Change of institution/course (International)" + } + + # Course mapping (simplified - showing most common ones) + course_map = { + 33: "Biofuel Production Technologies", + 171: "Animation and Multimedia Design", + 8014: "Social Service (evening attendance)", + 9003: "Agronomy", + 9070: "Communication Design", + 9085: "Veterinary Nursing", + 9119: "Informatics Engineering", + 9130: "Equinculture", + 9147: "Management", + 9238: "Social Service", + 9254: "Tourism", + 9500: "Nursing", + 9556: "Oral Hygiene", + 9670: "Advertising and Marketing Management", + 9773: "Journalism and Communication", + 9853: "Basic Education", + 9991: "Management (evening attendance)" + } + + # Previous qualification mapping + qualification_map = { + 1: "Secondary education", + 2: "Higher education - bachelor's degree", + 3: "Higher education - degree", + 4: "Higher education - master's", + 5: "Higher education - doctorate", + 6: "Frequency of higher education", + 9: "12th year of schooling - not completed", + 10: "11th year of schooling - not completed", + 12: "Other - 11th year of schooling", + 14: "10th year of schooling", + 15: "10th year of schooling - not completed", + 19: "Basic education 3rd cycle (9th/10th/11th year) or equiv.", + 38: "Basic education 2nd cycle (6th/7th/8th year) or equiv.", + 39: "Technological specialization course", + 40: "Higher education - degree (1st cycle)", + 42: "Professional course", + 43: "Higher education - master (2nd cycle)" + } + + # Occupation mapping (simplified) + occupation_map = { + 0: "Student", + 1: "Representatives of the Legislative Power and Executive Bodies", + 2: "Specialists in Intellectual and Scientific Activities", + 3: "Intermediate Level Technicians and Professions", + 4: "Administrative staff", + 5: "Personal Services, Security and Safety Workers", + 6: "Farmers and Skilled Workers in Agriculture", + 7: "Skilled Workers in Industry, Construction and Craftsmen", + 8: "Installation and Machine Operators and Assembly Workers", + 9: "Unskilled Workers", + 10: "Armed Forces Professions", + 90: "Other Situation", + 99: "Unspecified", + 122: "Health professionals", + 123: "Teachers", + 125: "Specialists in information and communication technologies", + 131: "Intermediate level science and engineering technicians", + 132: "Technicians and associate professionals, health", + 134: "Intermediate level technicians from legal, social, sports", + 141: "Office workers, secretaries in general and data processing", + 143: "Data, accounting, statistical, financial services", + 144: "Other administrative support staff", + 151: "Personal service workers", + 152: "Sellers", + 153: "Personal care workers and the like", + 171: "Skilled construction workers and the like, except electricians", + 173: "Skilled workers in printing, precision instrument manufacturing", + 175: "Workers in food processing, woodworking, clothing", + 191: "Cleaning workers", + 192: "Unskilled workers in agriculture, animal production", + 193: "Unskilled workers in extractive industry, construction", + 194: "Meal preparation assistants" + } + + return { + 'marital_status': marital_status_map, + 'application_mode': application_mode_map, + 'course': course_map, + 'qualification': qualification_map, + 'occupation': occupation_map + } + +def preprocess_input(user_input, preprocessing_info): + """Preprocess user input to match the training data format""" + + # Create a DataFrame with the user input + input_df = pd.DataFrame([user_input]) + + # Get categorical columns that need one-hot encoding + categorical_columns = [ + 'Marital status', 'Application mode', 'Course', 'Daytime/evening attendance', + 'Previous qualification', 'Nationality', 'Mothers qualification', + 'Fathers qualification', 'Mothers occupation', 'Fathers occupation', + 'Gender', 'Displaced', 'Educational special needs', 'Debtor', + 'Tuition fees up to date', 'Scholarship holder', 'International', + 'Application order' + ] + + # Perform one-hot encoding for categorical columns + for column in categorical_columns: + if column in input_df.columns: + # Get dummies with prefix + dummies = pd.get_dummies(input_df[column], prefix=column) + # Concatenate with original data + input_df = pd.concat([input_df, dummies], axis=1) + # Drop original column + input_df.drop(columns=[column], inplace=True) + + # Ensure all training features are present + feature_names = preprocessing_info['feature_names'] + + # Add missing columns with 0 values + for feature in feature_names: + if feature not in input_df.columns: + input_df[feature] = 0 + + # Select only the features used in training and in the same order + input_df = input_df[feature_names] + + return input_df + +def main(): + """Main Streamlit application""" + + # Load model and preprocessing info + model, preprocessing_info, feature_names = load_model_and_info() + + # Create feature mappings for user-friendly display + feature_maps = create_feature_mappings() + + # Header + st.markdown('

๐ŸŽ“ Student Success Predictor

', unsafe_allow_html=True) + st.markdown("**Predict whether a student will Graduate, Dropout, or remain Enrolled based on academic and demographic factors.**") + + # Sidebar for input + st.sidebar.markdown('

๐Ÿ“ Student Information

', unsafe_allow_html=True) + + # Create input form + with st.sidebar: + # Personal Information + st.markdown("### ๐Ÿ‘ค Personal Information") + + marital_status = st.selectbox( + "Marital Status", + options=list(feature_maps['marital_status'].keys()), + format_func=lambda x: feature_maps['marital_status'][x] + ) + + gender = st.selectbox("Gender", [0, 1], format_func=lambda x: "Female" if x == 0 else "Male") + age_at_enrollment = st.slider("Age at Enrollment", 17, 70, 20) + nationality = st.selectbox("Nationality", [1, 2, 6, 11, 13, 14, 17, 21, 22, 24, 25, 26, 32, 41, 62, 100, 101, 103, 105, 108, 109]) + + # Academic Background + st.markdown("### ๐ŸŽ“ Academic Background") + + application_mode = st.selectbox( + "Application Mode", + options=list(feature_maps['application_mode'].keys()), + format_func=lambda x: feature_maps['application_mode'].get(x, f"Mode {x}") + ) + + application_order = st.slider("Application Order", 0, 9, 1) + + course = st.selectbox( + "Course", + options=list(feature_maps['course'].keys()), + format_func=lambda x: feature_maps['course'].get(x, f"Course {x}") + ) + + daytime_evening = st.selectbox("Attendance", [0, 1], format_func=lambda x: "Evening" if x == 0 else "Daytime") + + previous_qualification = st.selectbox( + "Previous Qualification", + options=list(feature_maps['qualification'].keys()), + format_func=lambda x: feature_maps['qualification'].get(x, f"Qualification {x}") + ) + + previous_qualification_grade = st.slider("Previous Qualification Grade", 95, 190, 120) + admission_grade = st.slider("Admission Grade", 95, 190, 120) + + # Family Background + st.markdown("### ๐Ÿ‘จโ€๐Ÿ‘ฉโ€๐Ÿ‘งโ€๐Ÿ‘ฆ Family Background") + + mothers_qualification = st.selectbox( + "Mother's Qualification", + options=list(feature_maps['qualification'].keys()), + format_func=lambda x: feature_maps['qualification'].get(x, f"Qualification {x}") + ) + + fathers_qualification = st.selectbox( + "Father's Qualification", + options=list(feature_maps['qualification'].keys()), + format_func=lambda x: feature_maps['qualification'].get(x, f"Qualification {x}") + ) + + mothers_occupation = st.selectbox( + "Mother's Occupation", + options=list(feature_maps['occupation'].keys()), + format_func=lambda x: feature_maps['occupation'].get(x, f"Occupation {x}") + ) + + fathers_occupation = st.selectbox( + "Father's Occupation", + options=list(feature_maps['occupation'].keys()), + format_func=lambda x: feature_maps['occupation'].get(x, f"Occupation {x}") + ) + + # Financial and Social Factors + st.markdown("### ๐Ÿ’ฐ Financial & Social") + + displaced = st.selectbox("Displaced", [0, 1], format_func=lambda x: "No" if x == 0 else "Yes") + educational_special_needs = st.selectbox("Educational Special Needs", [0, 1], format_func=lambda x: "No" if x == 0 else "Yes") + debtor = st.selectbox("Debtor", [0, 1], format_func=lambda x: "No" if x == 0 else "Yes") + tuition_fees_up_to_date = st.selectbox("Tuition Fees Up to Date", [0, 1], format_func=lambda x: "No" if x == 0 else "Yes") + scholarship_holder = st.selectbox("Scholarship Holder", [0, 1], format_func=lambda x: "No" if x == 0 else "Yes") + international = st.selectbox("International Student", [0, 1], format_func=lambda x: "No" if x == 0 else "Yes") + + # Academic Performance - 1st Semester + st.markdown("### ๐Ÿ“š 1st Semester Performance") + + curricular_units_1st_sem_credited = st.slider("Curricular Units 1st Sem (Credited)", 0, 26, 0) + curricular_units_1st_sem_enrolled = st.slider("Curricular Units 1st Sem (Enrolled)", 0, 26, 6) + curricular_units_1st_sem_evaluations = st.slider("Curricular Units 1st Sem (Evaluations)", 0, 45, 6) + curricular_units_1st_sem_approved = st.slider("Curricular Units 1st Sem (Approved)", 0, 26, 6) + curricular_units_1st_sem_grade = st.slider("Curricular Units 1st Sem (Grade)", 0, 20, 13) + curricular_units_1st_sem_without_evaluations = st.slider("Curricular Units 1st Sem (Without Evaluations)", 0, 14, 0) + + # Academic Performance - 2nd Semester + st.markdown("### ๐Ÿ“– 2nd Semester Performance") + + curricular_units_2nd_sem_credited = st.slider("Curricular Units 2nd Sem (Credited)", 0, 20, 0) + curricular_units_2nd_sem_enrolled = st.slider("Curricular Units 2nd Sem (Enrolled)", 0, 23, 6) + curricular_units_2nd_sem_evaluations = st.slider("Curricular Units 2nd Sem (Evaluations)", 0, 33, 6) + curricular_units_2nd_sem_approved = st.slider("Curricular Units 2nd Sem (Approved)", 0, 20, 6) + curricular_units_2nd_sem_grade = st.slider("Curricular Units 2nd Sem (Grade)", 0, 20, 13) + curricular_units_2nd_sem_without_evaluations = st.slider("Curricular Units 2nd Sem (Without Evaluations)", 0, 12, 0) + + # Economic Indicators + st.markdown("### ๐Ÿ“Š Economic Indicators") + + unemployment_rate = st.slider("Unemployment Rate (%)", 7.0, 16.2, 10.8) + inflation_rate = st.slider("Inflation Rate (%)", -0.8, 3.7, 1.4) + gdp = st.slider("GDP", -4.06, 3.51, 1.74) + + # Create user input dictionary + user_input = { + 'Marital status': marital_status, + 'Application mode': application_mode, + 'Application order': application_order, + 'Course': course, + 'Daytime/evening attendance': daytime_evening, + 'Previous qualification': previous_qualification, + 'Previous qualification (grade)': previous_qualification_grade, + 'Nationality': nationality, + 'Mothers qualification': mothers_qualification, + 'Fathers qualification': fathers_qualification, + 'Mothers occupation': mothers_occupation, + 'Fathers occupation': fathers_occupation, + 'Admission grade': admission_grade, + 'Displaced': displaced, + 'Educational special needs': educational_special_needs, + 'Debtor': debtor, + 'Tuition fees up to date': tuition_fees_up_to_date, + 'Gender': gender, + 'Scholarship holder': scholarship_holder, + 'Age at enrollment': age_at_enrollment, + 'International': international, + 'Curricular units 1st sem (credited)': curricular_units_1st_sem_credited, + 'Curricular units 1st sem (enrolled)': curricular_units_1st_sem_enrolled, + 'Curricular units 1st sem (evaluations)': curricular_units_1st_sem_evaluations, + 'Curricular units 1st sem (approved)': curricular_units_1st_sem_approved, + 'Curricular units 1st sem (grade)': curricular_units_1st_sem_grade, + 'Curricular units 1st sem (without evaluations)': curricular_units_1st_sem_without_evaluations, + 'Curricular units 2nd sem (credited)': curricular_units_2nd_sem_credited, + 'Curricular units 2nd sem (enrolled)': curricular_units_2nd_sem_enrolled, + 'Curricular units 2nd sem (evaluations)': curricular_units_2nd_sem_evaluations, + 'Curricular units 2nd sem (approved)': curricular_units_2nd_sem_approved, + 'Curricular units 2nd sem (grade)': curricular_units_2nd_sem_grade, + 'Curricular units 2nd sem (without evaluations)': curricular_units_2nd_sem_without_evaluations, + 'Unemployment rate': unemployment_rate, + 'Inflation rate': inflation_rate, + 'GDP': gdp + } + + # Main content area + col1, col2 = st.columns([2, 1]) + + with col1: + st.markdown('

๐Ÿ”ฎ Prediction Results

', unsafe_allow_html=True) + + # Make prediction + if st.button("๐Ÿš€ Predict Student Outcome", type="primary"): + with st.spinner("Analyzing student data..."): + # Preprocess input + processed_input = preprocess_input(user_input, preprocessing_info) + + # Make prediction + prediction = model.predict(processed_input)[0] + prediction_proba = model.predict_proba(processed_input)[0] + + # Get prediction label + target_reverse_mapping = preprocessing_info['target_reverse_mapping'] + predicted_outcome = target_reverse_mapping[prediction] + + # Display prediction with styling + if predicted_outcome == "Graduate": + st.markdown(f'
๐ŸŽ“ Predicted Outcome: GRADUATE
', unsafe_allow_html=True) + elif predicted_outcome == "Dropout": + st.markdown(f'
โš ๏ธ Predicted Outcome: DROPOUT
', unsafe_allow_html=True) + else: + st.markdown(f'
๐Ÿ“š Predicted Outcome: ENROLLED
', unsafe_allow_html=True) + + # Display probabilities + st.markdown("### ๐Ÿ“Š Prediction Confidence") + + prob_df = pd.DataFrame({ + 'Outcome': ['Dropout', 'Enrolled', 'Graduate'], + 'Probability': prediction_proba + }) + + # Create probability chart + fig = px.bar( + prob_df, + x='Outcome', + y='Probability', + color='Outcome', + color_discrete_map={ + 'Graduate': '#28a745', + 'Dropout': '#dc3545', + 'Enrolled': '#ffc107' + }, + title="Prediction Probabilities" + ) + fig.update_layout(showlegend=False) + st.plotly_chart(fig, use_container_width=True) + + # Display detailed probabilities + col_a, col_b, col_c = st.columns(3) + + with col_a: + st.markdown(f""" +
+

๐ŸŽ“ Graduate

+

{prediction_proba[2]:.1%}

+
+ """, unsafe_allow_html=True) + + with col_b: + st.markdown(f""" +
+

โš ๏ธ Dropout

+

{prediction_proba[0]:.1%}

+
+ """, unsafe_allow_html=True) + + with col_c: + st.markdown(f""" +
+

๐Ÿ“š Enrolled

+

{prediction_proba[1]:.1%}

+
+ """, unsafe_allow_html=True) + + with col2: + st.markdown('

โ„น๏ธ About the Model

', unsafe_allow_html=True) + + st.info(""" + **๐Ÿค– Model Details:** + - **Algorithm:** Random Forest Classifier + - **Features:** 36 student characteristics + - **Training Accuracy:** 100% + - **Test Accuracy:** 93.84% + - **Classes:** Graduate, Dropout, Enrolled + """) + + st.markdown("### ๐Ÿ“ˆ Key Factors") + st.markdown(""" + The model considers: + - **Academic Performance** (grades, units) + - **Demographics** (age, family background) + - **Economic Indicators** (GDP, unemployment) + - **Social Factors** (scholarships, special needs) + """) + + st.markdown("### ๐ŸŽฏ Use Cases") + st.markdown(""" + - **Early Warning System** for at-risk students + - **Resource Allocation** planning + - **Academic Support** targeting + - **Institutional Planning** insights + """) + + # Model performance visualization + st.markdown("### ๐Ÿ“Š Model Performance") + + # Create a simple performance chart + metrics = ['Accuracy', 'Precision', 'Recall', 'F1-Score'] + values = [93.84, 92.96, 94.02, 93.15] # From the notebook results + + fig_perf = go.Figure(data=go.Scatterpolar( + r=values, + theta=metrics, + fill='toself', + line_color='#1f77b4' + )) + + fig_perf.update_layout( + polar=dict( + radialaxis=dict( + visible=True, + range=[0, 100] + )), + showlegend=False, + title="Model Performance Metrics", + height=300 + ) + + st.plotly_chart(fig_perf, use_container_width=True) + + # Footer + st.markdown("---") + st.markdown("*Developed with โค๏ธ using Streamlit and scikit-learn*") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/train_and_save_model.py b/train_and_save_model.py new file mode 100644 index 0000000..17063f4 --- /dev/null +++ b/train_and_save_model.py @@ -0,0 +1,163 @@ +""" +Model Training and Pickle Export Script +This script recreates the Random Forest model training process and saves the model and preprocessing pipeline. +""" + +import pandas as pd +import numpy as np +import pickle +from sklearn.ensemble import RandomForestClassifier +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import LabelEncoder +from sklearn.metrics import accuracy_score + +def load_and_preprocess_data(): + """Load and preprocess the data exactly as done in the notebook""" + # Load data + dataFileNamePath = "./data/data.csv" + masterRawData = pd.read_csv(dataFileNamePath) + data = masterRawData.copy() + + # Fix column name typo + data.rename(columns={'Nacionality': 'Nationality'}, inplace=True) + + # Remove apostrophes from column names + for column_name in data.columns: + if "'" in column_name: + new_column_name = column_name.replace("'", "") + data.rename(columns={column_name: new_column_name}, inplace=True) + + return data + +def encode_target_variable(data): + """Encode the target variable as done in the notebook""" + # Label mapping from the notebook analysis + target_mapping = {'Dropout': 0, 'Enrolled': 1, 'Graduate': 2} + data['Target_encoded'] = data['Target'].map(target_mapping) + return data + +def prepare_features_for_modeling(data): + """Prepare features exactly as done in the notebook""" + # Get all categorical columns that need one-hot encoding + categorical_columns = [ + 'Marital status', 'Application mode', 'Course', 'Daytime/evening attendance', + 'Previous qualification', 'Nationality', 'Mothers qualification', + 'Fathers qualification', 'Mothers occupation', 'Fathers occupation', + 'Gender', 'Displaced', 'Educational special needs', 'Debtor', + 'Tuition fees up to date', 'Scholarship holder', 'International' + ] + + # Additional columns that might be categorical (from notebook analysis) + for col in data.columns: + if col not in categorical_columns and col not in ['Target', 'Target_encoded']: + unique_vals = data[col].nunique() + # If low unique values, likely categorical + if unique_vals <= 20 and col not in ['Age at enrollment']: + categorical_columns.append(col) + + # Create a copy for processing + processed_data = data.copy() + + # Perform one-hot encoding for categorical columns + for column in categorical_columns: + if column in processed_data.columns: + # Get dummies with prefix + dummies = pd.get_dummies(processed_data[column], prefix=column) + # Concatenate with original data + processed_data = pd.concat([processed_data, dummies], axis=1) + # Drop original column + processed_data.drop(columns=[column], inplace=True) + + # Drop the original Target column, keep Target_encoded + processed_data.drop(columns=['Target'], inplace=True) + + return processed_data + +def train_and_save_model(): + """Train the Random Forest model and save both model and preprocessing info""" + + # Load and preprocess data + print("Loading and preprocessing data...") + data = load_and_preprocess_data() + + # Encode target variable + data = encode_target_variable(data) + + # Prepare features + processed_data = prepare_features_for_modeling(data) + + # Separate features and target + X = processed_data.drop('Target_encoded', axis=1) + y = processed_data['Target_encoded'] + + print(f"Final dataset shape: {X.shape}") + print(f"Features: {X.columns.tolist()}") + + # Split data (using 30% test size as in the notebook) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42) + + # Train Random Forest model + print("Training Random Forest model...") + rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42) + rf_classifier.fit(X_train, y_train) + + # Evaluate model + train_accuracy = rf_classifier.score(X_train, y_train) + test_accuracy = rf_classifier.score(X_test, y_test) + + print(f"Training Accuracy: {train_accuracy:.4f}") + print(f"Test Accuracy: {test_accuracy:.4f}") + + # Save the trained model + with open('random_forest_model.pkl', 'wb') as f: + pickle.dump(rf_classifier, f) + + # Save feature names for consistency + feature_names = X.columns.tolist() + with open('feature_names.pkl', 'wb') as f: + pickle.dump(feature_names, f) + + # Save preprocessing information + preprocessing_info = { + 'target_mapping': {'Dropout': 0, 'Enrolled': 1, 'Graduate': 2}, + 'target_reverse_mapping': {0: 'Dropout', 1: 'Enrolled', 2: 'Graduate'}, + 'categorical_columns': [ + 'Marital status', 'Application mode', 'Course', 'Daytime/evening attendance', + 'Previous qualification', 'Nationality', 'Mothers qualification', + 'Fathers qualification', 'Mothers occupation', 'Fathers occupation', + 'Gender', 'Displaced', 'Educational special needs', 'Debtor', + 'Tuition fees up to date', 'Scholarship holder', 'International', + 'Application order' # Adding this based on notebook analysis + ], + 'feature_names': feature_names, + 'original_columns': [ + 'Marital status', 'Application mode', 'Application order', 'Course', + 'Daytime/evening attendance', 'Previous qualification', + 'Previous qualification (grade)', 'Nationality', 'Mothers qualification', + 'Fathers qualification', 'Mothers occupation', 'Fathers occupation', + 'Admission grade', 'Displaced', 'Educational special needs', 'Debtor', + 'Tuition fees up to date', 'Gender', 'Scholarship holder', + 'Age at enrollment', 'International', 'Curricular units 1st sem (credited)', + 'Curricular units 1st sem (enrolled)', 'Curricular units 1st sem (evaluations)', + 'Curricular units 1st sem (approved)', 'Curricular units 1st sem (grade)', + 'Curricular units 1st sem (without evaluations)', + 'Curricular units 2nd sem (credited)', 'Curricular units 2nd sem (enrolled)', + 'Curricular units 2nd sem (evaluations)', 'Curricular units 2nd sem (approved)', + 'Curricular units 2nd sem (grade)', 'Curricular units 2nd sem (without evaluations)', + 'Unemployment rate', 'Inflation rate', 'GDP' + ] + } + + with open('preprocessing_info.pkl', 'wb') as f: + pickle.dump(preprocessing_info, f) + + print("Model and preprocessing info saved successfully!") + print("Files created:") + print("- random_forest_model.pkl") + print("- feature_names.pkl") + print("- preprocessing_info.pkl") + + return rf_classifier, preprocessing_info + +if __name__ == "__main__": + train_and_save_model() \ No newline at end of file