Pre Owned Car Price Predictor

models/PreOwnedCarPrediction/model.py

@@ -0,0 +1,31 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+import joblib
+
+class CarPriceModel:
+    def __init__(self):
+        self.model = LinearRegression()
+
+    def load_data(self, filepath):
+        data = pd.read_csv(filepath)
+        return data
+
+    def preprocess_data(self, data):
+        # Perform preprocessing steps here
+        X = data.drop('price', axis=1)  # Assuming 'price' is the target column
+        y = data['price']
+        return train_test_split(X, y, test_size=0.2, random_state=42)
+
+    def train(self, X_train, y_train):
+        self.model.fit(X_train, y_train)
+
+    def save_model(self, model_path):
+        joblib.dump(self.model, model_path)
+
+if __name__ == "__main__":
+    car_model = CarPriceModel()
+    data = car_model.load_data('data/car_data.csv')  # Example path
+    X_train, X_test, y_train, y_test = car_model.preprocess_data(data)
+    car_model.train(X_train, y_train)
+    car_model.save_model('saved_models/car_price_model.pkl')

models/PreOwnedCarPrediction/modelEvalution.py

@@ -0,0 +1,21 @@
+import joblib
+import pandas as pd
+from sklearn.metrics import mean_squared_error, r2_score
+
+class ModelEvaluator:
+    def __init__(self, model_path):
+        self.model = joblib.load(model_path)
+
+    def evaluate(self, X_test, y_test):
+        predictions = self.model.predict(X_test)
+        mse = mean_squared_error(y_test, predictions)
+        r2 = r2_score(y_test, predictions)
+        print("Mean Squared Error:", mse)
+        print("R^2 Score:", r2)
+
+if __name__ == "__main__":
+    data = pd.read_csv('data/car_data.csv')  # Load your test data
+    X_test = data.drop('price', axis=1)  # Adjust based on your dataset
+    y_test = data['price']
+    evaluator = ModelEvaluator('saved_models/car_price_model.pkl')
+    evaluator.evaluate(X_test, y_test)

models/PreOwnedCarPrediction/predict.py

@@ -0,0 +1,16 @@
+import joblib
+import pandas as pd
+
+class CarPricePredictor:
+    def __init__(self, model_path):
+        self.model = joblib.load(model_path)
+
+    def predict(self, input_data):
+        return self.model.predict(input_data)
+
+if __name__ == "__main__":
+    predictor = CarPricePredictor('saved_models/car_price_model.pkl')
+    # Example input data, replace with actual data
+    input_data = pd.DataFrame([[...]], columns=[...])  # Replace with actual column names
+    predictions = predictor.predict(input_data)
+    print(predictions)