Create model.py

model.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.ensemble import RandomForestRegressor
+import joblib
+
+# Load the dataset
+url = "https://raw.githubusercontent.com/manishkr1754/CarDekho_Used_Car_Price_Prediction/main/notebooks/data/cardekho_dataset.csv"
+df = pd.read_csv(url)
+
+# Preprocessing
+num_features = ['vehicle_age', 'km_driven', 'mileage', 'engine', 'max_power', 'seats']
+cat_features = ['brand', 'model', 'seller_type', 'fuel_type', 'transmission_type']
+
+# Define the target variable
+X = df[num_features + cat_features]
+y = df['selling_price']
+
+# Preprocessing pipeline
+numeric_transformer = StandardScaler()
+onehot_transformer = OneHotEncoder(handle_unknown='ignore')
+
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', numeric_transformer, num_features),
+        ('cat', onehot_transformer, cat_features)
+    ])
+
+# Create and train the model
+model = Pipeline(steps=[
+    ('preprocessor', preprocessor),
+    ('regressor', RandomForestRegressor(n_estimators=100, random_state=42))
+])
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+model.fit(X_train, y_train)
+
+# Save the model
+joblib.dump(model, 'random_forest_model.pkl')