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| import joblib | |
| import numpy as np | |
| from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier | |
| from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score | |
| class LoanApprovalModel: | |
| def __init__(self, model_type='random_forest'): | |
| self.model_type = model_type | |
| self.model = self._get_model() | |
| self.preprocessor = None | |
| self.feature_names = None | |
| def _get_model(self): | |
| if self.model_type == 'random_forest': | |
| return RandomForestClassifier(n_estimators=100, random_state=42) | |
| elif self.model_type == 'gradient_boosting': | |
| return GradientBoostingClassifier(random_state=42) | |
| else: | |
| raise ValueError(f"Unsupported model type: {self.model_type}") | |
| def train(self, preprocessor, X_train, y_train, feature_names=None): | |
| """Train the model""" | |
| self.preprocessor = preprocessor | |
| self.feature_names = feature_names | |
| # Transform the data | |
| X_train_processed = preprocessor.fit_transform(X_train) | |
| # Train the model | |
| self.model.fit(X_train_processed, y_train) | |
| def predict(self, X): | |
| """Make predictions""" | |
| X_processed = self.preprocessor.transform(X) | |
| return self.model.predict(X_processed) | |
| def predict_proba(self, X): | |
| """Get prediction probabilities""" | |
| X_processed = self.preprocessor.transform(X) | |
| return self.model.predict_proba(X_processed) | |
| def evaluate(self, X_test, y_test): | |
| """Evaluate model performance""" | |
| y_pred = self.predict(X_test) | |
| y_proba = self.predict_proba(X_test)[:, 1] | |
| metrics = { | |
| 'accuracy': accuracy_score(y_test, y_pred), | |
| 'precision': precision_score(y_test, y_pred), | |
| 'recall': recall_score(y_test, y_pred), | |
| 'f1': f1_score(y_test, y_pred), | |
| 'roc_auc': roc_auc_score(y_test, y_proba) | |
| } | |
| return metrics | |
| def save_model(self, filepath): | |
| """Save model to disk""" | |
| model_data = { | |
| 'model': self.model, | |
| 'preprocessor': self.preprocessor, | |
| 'feature_names': self.feature_names, | |
| 'model_type': self.model_type | |
| } | |
| joblib.dump(model_data, filepath) | |
| def load_model(cls, filepath): | |
| """Load model from disk""" | |
| model_data = joblib.load(filepath) | |
| model_instance = cls(model_type=model_data['model_type']) | |
| model_instance.model = model_data['model'] | |
| model_instance.preprocessor = model_data['preprocessor'] | |
| model_instance.feature_names = model_data['feature_names'] | |
| return model_instance |