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added model training file

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andwati
2025-09-28 16:37:10 +03:00
parent c515df9d47
commit 6e4d6c5c1f
8 changed files with 218 additions and 1 deletions
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.idea/.gitignore
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# Default ignored files
/shelf/
/workspace.xml
# Editor-based HTTP Client requests
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.idea/inspectionProfiles/profiles_settings.xml
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<component name="InspectionProjectProfileManager">
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<version value="1.0" />
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
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<option name="sdkName" value="Python 3.13 (student-retention)" />
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.13 (student-retention)" project-jdk-type="Python SDK" />
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.idea/modules.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
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<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/student-retention.iml" filepath="$PROJECT_DIR$/.idea/student-retention.iml" />
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.idea/student-retention.iml
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<?xml version="1.0" encoding="UTF-8"?>
<module type="PYTHON_MODULE" version="4">
<component name="NewModuleRootManager">
<content url="file://$MODULE_DIR$">
<excludeFolder url="file://$MODULE_DIR$/.venv" />
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<orderEntry type="jdk" jdkName="Python 3.13 (student-retention)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
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<component name="PyDocumentationSettings">
<option name="format" value="PLAIN" />
<option name="myDocStringFormat" value="Plain" />
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<component name="TestRunnerService">
<option name="PROJECT_TEST_RUNNER" value="py.test" />
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="" vcs="Git" />
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README.md
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# student retention
# student retention
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.
models/train_and_save_model.py
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"""
Model Training and Pickle Export Script
This script recreates the Random Forest model training process and saves the model and preprocessing pipeline.
"""
import pickle
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
def load_and_preprocess_data():
"""Load and preprocess the data exactly as done in the notebook"""
# Load data
data_file_name_path = "./data/data.csv"
master_raw_data = pd.read_csv(data_file_name_path)
data = master_raw_data.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()