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import joblib
import pandas as pd
# Load the model
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 = {
'Year': 2018,
'Kilometers_Driven': 30000,
'Fuel_Type': 'Petrol',
'Transmission': 'Manual',
'Owner_Type': 'First',
'Location': 'Mumbai',
'Engine CC': 1200,
'Power': 85,
'Seats': 5
}
# Make prediction
predicted_mileage, predicted_price = predict(data)
print(f"Predicted Mileage (Km/L): {predicted_mileage:.2f}")
print(f"Predicted Price: ₹{predicted_price:,.2f} Lakhs")
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