model_evaluation.py

from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score 
import math 
"""
Parameters: 
- true_values: Actual target values.
- predicted_values: Predicted target values by the model.
"""
def evaluate(true_values, predicted_values): 
    mae = mean_absolute_error(true_values, predicted_values) # Calculate Mean Absolute Error
    mse = mean_squared_error(true_values, predicted_values) # Calculate Mean Squared Error
    rmse = math.sqrt(mse)
    r2 = r2_score(true_values, predicted_values) # Calculate R-squared Score
    return mae, mse, rmse, r2 

def print_evaluation(true_values, predicted_values):
    mae, mse, rmse, r2 = evaluate(true_values, predicted_values)
    print(f"Mean Absolute Error (MAE): {mae:.4f}")
    print(f"Mean Squared Error (MSE): {mse:.4f}")
    print(f"Root Mean Squared Error (RMSE): {rmse:.4f}")
    print(f"R-squared (R2): {r2:.4f}")