Added the Feature to predict price

2 files changed, 46 insertions, 25 deletions

diff --git a/main.py b/main.py
index 6fbb0c2..3355b34 100644
--- a/main.py
+++ b/main.py
@@ -27,7 +27,7 @@ def preprocess_data(df):
     df['Seats'] = df['Seats'].fillna(df['Seats'].mode()[0])
 
     # Remove rows with missing target variable
-    df = df.dropna(subset=['Mileage Km/L'])
+    df = df.dropna(subset=['Mileage Km/L', 'Price'])
 
     # Remove outliers in 'Kilometers Driven'
     q1, q3 = df['Kilometers_Driven'].quantile([0.25, 0.75])
@@ -40,10 +40,10 @@ def preprocess_data(df):
     return df
 
 # Train Model
-def train_model(df):
+def train_model(df, target, model_name):
     # Features and target
-    X = df.drop(columns=['Mileage Km/L', 'Name', 'Price'])
-    y = df['Mileage Km/L']
+    X = df.drop(columns=['Mileage Km/L', 'Price', 'Name'])
+    y = df[target]
 
     # Split data
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
@@ -81,14 +81,15 @@ def train_model(df):
     y_pred = best_model.predict(X_test)
 
     # Evaluation
-    print("Model Performance:")
+    print(f"Model Performance for {target}:")
     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 'model.pkl'")
+    model_file = f'{model_name}.pkl'
+    joblib.dump(best_model, model_file)
+    print("Model saved as '{model_file}'")
 
 # Main Function
 def main():
@@ -98,10 +99,12 @@ def main():
 
     df = preprocess_data(df)
     print("Data preprocessing complete.")
-    print("Model is training...")
 
-    train_model(df)
-    print("Model is trained.")
+    print("Training mileage prediction model...")
+    train_model(df, target='Mileage Km/L', model_name='mileage_predictor')
+
+    print("Training price prediction model...")
+    train_model(df, target='Price', model_name='price_predictor')
 
 if __name__ == "__main__":
     main()
diff --git a/predict.py b/predict.py
index 707ef92..d7f3a9b 100644
--- a/predict.py
+++ b/predict.py
@@ -2,25 +2,43 @@ import joblib
 import pandas as pd
 
 # Load the model
-model = joblib.load('model.pkl')
+mileage_model = joblib.load('mileage_predictor.pkl')
+price_model = joblib.load('price_predictor.pkl')
+
+# Prepare input data for prediction
+def prepare_input(data_dict):
+    # Prepare a DataFrame from a dictionary of input data
+    input_df = pd.DataFrame([data_dict])
+    input_df['Car_Age'] = 2024 - input_df['Year']
+    input_df.drop(columns=['Year'], inplace=True)
+
+    return input_df
+
+# Make prediction
+def predict(input_data):
+    # Predict mileage and price for a given input.
+    prepared_data = prepare_input(input_data)
+    mileage = mileage_model.predict(prepared_data)[0]
+    price = price_model.predict(prepared_data)[0]
+    
+    return mileage, price
 
 # Sample data for prediction
 data = {
-    'Kilometers_Driven': [50000],
-    'Fuel_Type': ['Petrol'],
-    'Transmission': ['Manual'],
-    'Owner_Type': ['First'],
-    'Engine CC': [1197],
-    'Power': [82],
-    'Seats': [5],
-    'Car_Age': [6],
-    'Location': ['Mumbai']
+    'Year': 2018,
+    'Kilometers_Driven': 30000,
+    'Fuel_Type': 'Petrol',
+    'Transmission': 'Manual',
+    'Owner_Type': 'First',
+    'Location': 'Mumbai',
+    'Engine CC': 1200,
+    'Power': 85,
+    'Seats': 5
 }
 
-# Convert to DataFrame
-input_data = pd.DataFrame(data)
+# Make prediction 
 
-# Predict
-predicted_mileage = model.predict(input_data)
+predicted_mileage, predicted_price = predict(data)
 
-print(f"Predicted Mileage: {predicted_mileage[0]:.2f} Km/L")
+print(f"Predicted Mileage (Km/L): {predicted_mileage:.2f}")
+print(f"Predicted Price: ₹{predicted_price:,.2f} Lakhs")