"""
Linear Regression Example

Author: RSK World
Website: https://rskworld.in
Email: help@rskworld.in
Phone: +91 93305 39277
"""

import numpy as np
import sys
import os

# Add parent directory to path
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from regression_analysis import LinearRegressionModel
from statistical_diagnostics import ModelDiagnostics


def main():
    print("Linear Regression Example")
    print("=" * 70)
    
    # Generate sample data
    np.random.seed(42)
    n = 100
    X = np.random.randn(n, 3)
    y = 2 + 1.5 * X[:, 0] + 0.8 * X[:, 1] - 0.5 * X[:, 2] + np.random.randn(n) * 0.5
    
    # Create and fit model
    model = LinearRegressionModel()
    model.fit(X, y)
    
    # Print summary
    print("\nModel Summary:")
    model.summary()
    
    # Diagnostic checks
    print("\n" + "=" * 70)
    print("Diagnostic Checks:")
    print("=" * 70)
    
    model.check_multicollinearity()
    model.check_heteroscedasticity()
    model.check_autocorrelation()
    
    # Comprehensive diagnostics
    print("\n" + "=" * 70)
    print("Comprehensive Diagnostics:")
    print("=" * 70)
    
    diagnostics = ModelDiagnostics(model.results)
    diagnostics.comprehensive_diagnostics()
    
    # Make predictions
    print("\n" + "=" * 70)
    print("Predictions:")
    print("=" * 70)
    
    X_new = np.random.randn(5, 3)
    predictions = model.predict(X_new)
    print(f"New data predictions:\n{predictions}")


if __name__ == "__main__":
    main()