1 files changed, 33 insertions, 15 deletions
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")