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import streamlit as st
import pandas as pd
import numpy as np
from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
# === Load dataset ===
@st.cache_data
def load_data():
df = pd.read_csv("src/Car.csv") # Make sure this file exists in your directory
return df
df = load_data()
st.title("🚗 Car Price Predictor App")
# === Data Preprocessing ===
X = df.drop(columns=['selling_price'])
y = df['selling_price']
# Manually defined categorical columns
categorical_cols = ['name', 'fuel', 'seller_type', 'transmission', 'owner']
numerical_cols = [col for col in X.columns if col not in categorical_cols]
# Label Encoding
label_encoders = {}
for col in categorical_cols:
le = LabelEncoder()
X[col] = le.fit_transform(X[col])
label_encoders[col] = le
# Scaling
scaler = StandardScaler()
X[numerical_cols] = scaler.fit_transform(X[numerical_cols])
# Train Model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = Ridge(alpha=1.0)
model.fit(X_train, y_train)
# === Input UI ===
st.sidebar.header("Enter Car Details")
car_name = st.sidebar.selectbox("Car Name", df['name'].unique())
year = st.sidebar.number_input("Year", min_value=1990, max_value=2025, step=1)
km_driven = st.sidebar.number_input("Kilometers Driven", min_value=0)
fuel = st.sidebar.selectbox("Fuel Type", df['fuel'].unique())
seller_type = st.sidebar.selectbox("Seller Type", df['seller_type'].unique())
transmission = st.sidebar.selectbox("Transmission", df['transmission'].unique())
owner = st.sidebar.selectbox("Owner Type", df['owner'].unique())
mileage = st.sidebar.number_input("Mileage (kmpl)", min_value=0.0)
engine = st.sidebar.number_input("Engine (CC)", min_value=500.0)
seats = st.sidebar.number_input("Seats", min_value=2, max_value=10)
# === Predict Button ===
if st.sidebar.button("Predict Price"):
input_df = pd.DataFrame({
'name': [car_name],
'year': [year],
'km_driven': [km_driven],
'fuel': [fuel],
'seller_type': [seller_type],
'transmission': [transmission],
'owner': [owner],
'mileage': [mileage],
'engine': [engine],
'seats': [seats]
})
# Apply Label Encoding
for col in categorical_cols:
le = label_encoders[col]
input_df[col] = le.transform(input_df[col])
# Apply Scaling
input_df[numerical_cols] = scaler.transform(input_df[numerical_cols])
# Predict
pred_price = model.predict(input_df)[0]
pred_price = max(0, round(pred_price))
st.success(f"💰 Predicted Selling Price: ₹{pred_price}")
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