path: root/predict.py

import joblib
import pandas as pd

# Load the model
mileage_model = joblib.load('mileage_predictor.pkl')
price_model = joblib.load('price_predictor.pkl')
year_model = joblib.load('year_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]
    year = year_model.predict(prepared_data)[0]
    
    return mileage, price, int(year)

def collect_input():
    print("Enter the car details for prediction:")
    year = int(input("Year of Manufacture (e.g., 2022): "))
    kilometers_driven = int(input("Kilometers Driven (e.g., 30000): "))
    fuel_type = input("Fuel Type (Petrol/Diesel/CNG/Electric): ")
    transmission = input("Transmission (Manual/Automatic): ")
    owner_type = input("Owner Type (First/Second/Third/Fourth & Above): ")
    location = input("Location (e.g., Mumbai): ")
    engine_cc = int(input("Engine Capacity in CC (e.g., 1200): "))
    power = float(input("Power in BHP (e.g., 85): "))
    seats = int(input("Number of Seats (e.g., 5): "))

    return {
        'Year': year,
        'Kilometers_Driven': kilometers_driven,
        'Fuel_Type': fuel_type,
        'Transmission': transmission,
        'Owner_Type': owner_type,
        'Location': location,
        'Engine CC': engine_cc,
        'Power': power,
        'Seats': seats
    }


# Collect input data from the user
data = collect_input()

# Make prediction 
predicted_mileage, predicted_price, predicted_year = predict(data)

print("\nPrediction Results:")
print(f"Predicted Mileage (Km/L): {predicted_mileage:.2f}")
print(f"Predicted Price: ₹{predicted_price:,.2f} Lakhs")
print(f"Predicted Year: {predicted_year}")