updated requirements

README.md

@@ -1,207 +1,3 @@
 # 🎓 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.
-
-## 🚀 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 app.py
-```
-
-The dashboard will open in your browser at `http://localhost:8501`
-
-## 📊 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%)
-
-## 🎯 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
-
-## 🔧 Technical Details
-
-### Data Pipeline
-1. **Data Loading:** Reads from `./data/data.csv`
-2. **Column Cleaning:** Removes apostrophes, fixes typos (Nacionality → 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
-
-## 📁 Project Structure
-
-```
-predictive_analysis/
-├── data/
-│   └── data.csv                    # Original dataset (4,424 students)
-├── src/                            # Source code modules
-│   ├── config/                     # Configuration files
-│   │   ├── __init__.py
-│   │   ├── theme.py               # Rose Pine theme and constants
-│   │   └── mappings.py            # Feature mappings and presets
-│   ├── utils/                     # Utility functions
-│   │   ├── __init__.py
-│   │   └── model_utils.py         # Model loading and preprocessing
-│   ├── components/                # UI components
-│   │   ├── __init__.py
-│   │   ├── form_components.py     # Input form sections
-│   │   └── prediction_components.py # Results and charts
-│   └── __init__.py
-├── predictive_analysis.ipynb      # Original analysis notebook
-├── train_and_save_model.py        # Model training and pickle export
-├── streamlit_app.py               # Legacy monolithic app (deprecated)
-├── app.py                         # New modular main application
-├── requirements.txt               # Python dependencies
-├── random_forest_model.pkl        # Trained model (generated)
-├── preprocessing_info.pkl         # Preprocessing pipeline (generated)
-├── feature_names.pkl              # Feature list (generated)
-└── README.md                      # This file
-```
-
-## 🎨 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
-
-## 🔍 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
-
-## 🚀 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"]
-```
-
-## 🛠️ 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
-
-## 📊 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
-
-## 🤝 Contributing
-
-1. Fork the repository
-2. Create a feature branch
-3. Add tests for new functionality
-4. Submit a pull request
-
-## 📄 License
-
-This project is open source and available under the MIT License.
-
-## 🙏 Acknowledgments
-
-- Dataset from UCI Machine Learning Repository
-- Built with Streamlit, scikit-learn, and Plotly
-- Inspired by educational data mining research

requirements.txt

@@ -1,6 +1,5 @@
-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
+streamlit==1.49.1
+pandas==2.3.2
+numpy==2.2.5
+scikit-learn==1.7.1
+plotly==6.3.0