Data Pridiction of Cars

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+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+import joblib
+
+# Load Dataset
+def load_data(file_path):
+    df = pd.read_csv(file_path)
+    # Drop unnecessary index column if present
+    df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
+    return df
+
+# Preprocessing
+def preprocess_data(df):
+    # Calculate Car Age
+    df['Car_Age'] = 2024 - df['Year']
+    df.drop(columns=['Year'], inplace=True)
+
+    # Handle missing values
+    df['Engine CC'] = df['Engine CC'].fillna(df['Engine CC'].median())
+    df['Power'] = df['Power'].fillna(df['Power'].median())
+    df['Seats'] = df['Seats'].fillna(df['Seats'].mode()[0])
+
+    # Remove rows with missing target variable
+    df = df.dropna(subset=['Mileage Km/L'])
+
+    # Remove outliers in 'Kilometers Driven'
+    q1, q3 = df['Kilometers_Driven'].quantile([0.25, 0.75])
+    iqr = q3 - q1
+    lower_bound = q1 - 1.5 * iqr
+    upper_bound = q3 + 1.5 * iqr
+    df = df[(df['Kilometers_Driven'] >= lower_bound) & (df['Kilometers_Driven'] <= upper_bound)]
+
+    # Return processed dataframe
+    return df
+
+# Train Model
+def train_model(df):
+    # Features and target
+    X = df.drop(columns=['Mileage Km/L', 'Name', 'Price'])
+    y = df['Mileage Km/L']
+
+    # Split data
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # ColumnTransformer for preprocessing
+    categorical_cols = ['Fuel_Type', 'Transmission', 'Owner_Type', 'Location']
+    numerical_cols = ['Kilometers_Driven', 'Engine CC', 'Power', 'Seats', 'Car_Age']
+
+    preprocessor = ColumnTransformer(
+        transformers=[
+            ('num', StandardScaler(), numerical_cols),
+            ('cat', OneHotEncoder(drop='first'), categorical_cols)
+        ]
+    )
+
+    # Random Forest Regressor pipeline
+    rf_pipeline = Pipeline([
+        ('preprocessor', preprocessor),
+        ('regressor', RandomForestRegressor(random_state=42))
+    ])
+
+    # Hyperparameter tuning
+    param_grid = {
+        'regressor__n_estimators': [50, 100, 200],
+        'regressor__max_depth': [10, 20, None]
+    }
+
+    grid_search = GridSearchCV(rf_pipeline, param_grid, cv=5, scoring='neg_mean_squared_error')
+    grid_search.fit(X_train, y_train)
+
+    # Best model
+    best_model = grid_search.best_estimator_
+
+    # Test predictions
+    y_pred = best_model.predict(X_test)
+
+    # Evaluation
+    print("Model Performance:")
+    print(f"MAE: {mean_absolute_error(y_test, y_pred):.2f}")
+    print(f"RMSE: {mean_squared_error(y_test, y_pred, squared=False):.2f}")
+    print(f"R^2: {r2_score(y_test, y_pred):.2f}")
+
+    # Save the model
+    joblib.dump(best_model, 'model.pkl')
+    print("Model saved as 'best_mileage_predictor.pkl'")
+
+# Main Function
+def main():
+    file_path = 'data.csv'  # Update with your dataset file path
+    df = load_data(file_path)
+    print("Dataset loaded.")
+
+    df = preprocess_data(df)
+    print("Data preprocessing complete.")
+
+    train_model(df)
+
+if __name__ == "__main__":
+    main()