Vaibhavkate/Bmw_Car_Prediction

import streamlit as st import pandas as pd import joblib

Load Model

model = joblib.load("LR_BMW.pkl") scaler = joblib.load("scaler.pkl") columns = joblib.load("columns.pkl")

st.set_page_config( page_title="BMW Car Price Prediction", page_icon="🚗", layout="centered" )

st.title("🚗 BMW Car Price Prediction")

st.markdown("Enter Car Details")

Inputs

year = st.number_input("Year", 1990, 2035, 2018)

mileage = st.number_input("Mileage", 0, 300000, 30000)

tax = st.number_input("Tax", 0, 1000, 150)

mpg = st.number_input("MPG", 0.0, 150.0, 55.4)

engineSize = st.number_input("Engine Size", 0.5, 8.0, 2.0)

model_name = st.selectbox( "Select BMW Model", [ "1 Series", "2 Series", "3 Series", "4 Series", "5 Series", "6 Series", "7 Series", "8 Series", "X1", "X2", "X3", "X4", "X5", "X6", "X7", "Z3", "Z4", "M2", "M3", "M4", "M5", "M6", "i3", "i8" ] )

transmission = st.selectbox( "Transmission", ["Manual", "Automatic", "Semi-Auto"] )

fuelType = st.selectbox( "Fuel Type", ["Petrol", "Diesel", "Hybrid", "Electric"] )

Prediction

if st.button("Predict Price"):

input_df = pd.DataFrame([{
    "year": year,
    "mileage": mileage,
    "tax": tax,
    "mpg": mpg,
    "engineSize": engineSize,
    "model": model_name,
    "transmission": transmission,
    "fuelType": fuelType
}])

input_df = pd.get_dummies(input_df, drop_first=True)

input_df = input_df.reindex(columns=columns, fill_value=0)

input_scaled = scaler.transform(input_df)

prediction = model.predict(input_scaled)

st.success(f"Estimated Price: £ {prediction[0]:,.2f}")