""" Housing Price Prediction Dataset - Advanced ML Models RSK World - Free Programming Resources & Source Code Website: https://rskworld.in Contact: help@rskworld.in, support@rskworld.in Phone: +91 93305 39277 Founder: Molla Samser Designer & Tester: Rima Khatun Created: 2026 """ import pandas as pd import numpy as np from sklearn.model_selection import train_test_split, cross_val_score, KFold from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor from sklearn.tree import DecisionTreeRegressor from sklearn.svm import SVR from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error, mean_absolute_percentage_error import joblib import os from datetime import datetime # Try importing XGBoost (optional) try: import xgboost as xgb XGBOOST_AVAILABLE = True except ImportError: XGBOOST_AVAILABLE = False print("Note: XGBoost not available. Install with: pip install xgboost") # Try importing LightGBM (optional) try: import lightgbm as lgb LIGHTGBM_AVAILABLE = True except ImportError: LIGHTGBM_AVAILABLE = False print("Note: LightGBM not available. Install with: pip install lightgbm") class AdvancedHousingPricePredictor: """Advanced housing price prediction with multiple ML models""" def __init__(self, data_path='housing_prices.csv'): """Initialize the predictor with data""" print("Loading dataset...") self.df = pd.read_csv(data_path) self.models = {} self.results = {} self.best_model = None self.best_score = float('inf') def prepare_features(self): """Prepare features for modeling""" print("\nPreparing features...") # Select numerical features feature_columns = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'sqft_living15', 'sqft_lot15'] X = self.df[feature_columns].copy() y = self.df['price'].copy() # Feature engineering: Age of house current_year = 2026 X['house_age'] = current_year - X['yr_built'] X['renovation_age'] = X['yr_renovated'].apply(lambda x: current_year - x if x > 0 else 0) # Feature engineering: Total square feet X['total_sqft'] = X['sqft_above'] + X['sqft_basement'] # Feature engineering: Price per square foot (for normalization) X['bedroom_bathroom_ratio'] = X['bedrooms'] / (X['bathrooms'] + 0.1) # Add small value to avoid division by zero # Feature engineering: Lot ratio X['lot_ratio'] = X['sqft_lot'] / (X['sqft_living'] + 1) self.feature_columns = list(X.columns) self.X = X self.y = y print(f"Features prepared: {X.shape[1]} features, {X.shape[0]} samples") return X, y def initialize_models(self): """Initialize multiple ML models""" print("\nInitializing ML models...") self.models = { 'Linear Regression': LinearRegression(), 'Ridge Regression': Ridge(alpha=1.0), 'Lasso Regression': Lasso(alpha=1.0), 'Elastic Net': ElasticNet(alpha=1.0, l1_ratio=0.5), 'Decision Tree': DecisionTreeRegressor(random_state=42, max_depth=10), 'Random Forest': RandomForestRegressor(n_estimators=100, random_state=42, max_depth=10), 'Gradient Boosting': GradientBoostingRegressor(n_estimators=100, random_state=42, max_depth=5), 'AdaBoost': AdaBoostRegressor(n_estimators=50, random_state=42), 'SVR': SVR(kernel='rbf', C=100, gamma='scale') } if XGBOOST_AVAILABLE: self.models['XGBoost'] = xgb.XGBRegressor(n_estimators=100, random_state=42, max_depth=5) if LIGHTGBM_AVAILABLE: self.models['LightGBM'] = lgb.LGBMRegressor(n_estimators=100, random_state=42, max_depth=5, verbose=-1) print(f"Initialized {len(self.models)} models") def train_and_evaluate(self, test_size=0.2, cv_folds=5): """Train and evaluate all models""" print("\n" + "="*60) print("Training and Evaluating Models") print("="*60) X_train, X_test, y_train, y_test = train_test_split( self.X, self.y, test_size=test_size, random_state=42 ) self.X_train = X_train self.X_test = X_test self.y_train = y_train self.y_test = y_test kfold = KFold(n_splits=cv_folds, shuffle=True, random_state=42) for name, model in self.models.items(): print(f"\nTraining {name}...") # Train model model.fit(X_train, y_train) # Predictions y_train_pred = model.predict(X_train) y_test_pred = model.predict(X_test) # Metrics train_rmse = np.sqrt(mean_squared_error(y_train, y_train_pred)) test_rmse = np.sqrt(mean_squared_error(y_test, y_test_pred)) train_r2 = r2_score(y_train, y_train_pred) test_r2 = r2_score(y_test, y_test_pred) train_mae = mean_absolute_error(y_train, y_train_pred) test_mae = mean_absolute_error(y_test, y_test_pred) test_mape = mean_absolute_percentage_error(y_test, y_test_pred) * 100 # Cross-validation cv_scores = cross_val_score(model, X_train, y_train, cv=kfold, scoring='neg_mean_squared_error') cv_rmse = np.sqrt(-cv_scores.mean()) cv_std = np.sqrt(cv_scores.std()) # Store results self.results[name] = { 'model': model, 'train_rmse': train_rmse, 'test_rmse': test_rmse, 'train_r2': train_r2, 'test_r2': test_r2, 'train_mae': train_mae, 'test_mae': test_mae, 'test_mape': test_mape, 'cv_rmse': cv_rmse, 'cv_std': cv_std } print(f" Test RMSE: ${test_rmse:,.2f}") print(f" Test R²: {test_r2:.4f}") print(f" Test MAE: ${test_mae:,.2f}") print(f" CV RMSE: ${cv_rmse:,.2f} (±${cv_std:,.2f})") # Track best model if test_rmse < self.best_score: self.best_score = test_rmse self.best_model = name def print_summary(self): """Print summary of all models""" print("\n" + "="*60) print("MODEL COMPARISON SUMMARY") print("="*60) summary_data = [] for name, metrics in self.results.items(): summary_data.append({ 'Model': name, 'Test RMSE': f"${metrics['test_rmse']:,.2f}", 'Test R²': f"{metrics['test_r2']:.4f}", 'Test MAE': f"${metrics['test_mae']:,.2f}", 'Test MAPE': f"{metrics['test_mape']:.2f}%", 'CV RMSE': f"${metrics['cv_rmse']:,.2f}", 'CV Std': f"${metrics['cv_std']:,.2f}" }) summary_df = pd.DataFrame(summary_data) summary_df = summary_df.sort_values('Model') print(summary_df.to_string(index=False)) print(f"\n{'='*60}") print(f"BEST MODEL: {self.best_model}") print(f"Best Test RMSE: ${self.best_score:,.2f}") print(f"{'='*60}") def save_models(self, directory='models'): """Save all trained models""" os.makedirs(directory, exist_ok=True) print(f"\nSaving models to '{directory}' directory...") for name, metrics in self.results.items(): model = metrics['model'] filename = os.path.join(directory, f"{name.lower().replace(' ', '_')}.pkl") joblib.dump(model, filename) print(f" Saved: {filename}") # Save feature columns joblib.dump(self.feature_columns, os.path.join(directory, 'feature_columns.pkl')) print(f"\nAll models saved successfully!") def get_feature_importance(self, model_name='Random Forest'): """Get feature importance for tree-based models""" if model_name not in self.results: print(f"Model {model_name} not found") return None model = self.results[model_name]['model'] if hasattr(model, 'feature_importances_'): importance_df = pd.DataFrame({ 'Feature': self.feature_columns, 'Importance': model.feature_importances_ }).sort_values('Importance', ascending=False) print(f"\n{'='*60}") print(f"FEATURE IMPORTANCE - {model_name}") print(f"{'='*60}") print(importance_df.to_string(index=False)) return importance_df else: print(f"{model_name} does not support feature importance") return None def main(): """Main execution function""" print("="*60) print("Advanced Housing Price Prediction Models") print("RSK World - Free Programming Resources & Source Code") print("="*60) # Initialize predictor predictor = AdvancedHousingPricePredictor() # Prepare features predictor.prepare_features() # Initialize models predictor.initialize_models() # Train and evaluate predictor.train_and_evaluate(cv_folds=5) # Print summary predictor.print_summary() # Feature importance predictor.get_feature_importance('Random Forest') if XGBOOST_AVAILABLE: predictor.get_feature_importance('XGBoost') # Save models predictor.save_models() print("\n" + "="*60) print("Analysis Complete!") print("="*60) if __name__ == "__main__": main()