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🚗 Car Price Prediction using Machine Learning

This project predicts the selling price of a used car based on features such as year, fuel type, kilometers driven, seller type, and more.

📂 Files Included

  • cars.csv — The dataset containing used car listings with features like year, present price, kms driven, fuel type, seller type, transmission, etc.
  • Car_Price_Prediction.ipynb — Jupyter Notebook for:
    • Exploratory Data Analysis (EDA)
    • Data preprocessing (handling categorical features using OneHotEncoding)
    • Model training using Linear Regression
    • Evaluation and visualization
  • model.pkl — The trained machine learning model saved using joblib.
  • ss.pkl — StandardScaler object used to scale input features before prediction.
  • testing_on_newdata.ipynb — A separate notebook used to test the saved model on new data inputs.

🧠 Model Overview

  • Model Used: Linear Regression
  • Target Variable: Selling Price
  • Features Used:
    • Year
    • Present Price
    • Kms Driven
    • Fuel Type (converted using one-hot encoding)
    • Seller Type
    • Transmission

📈 Example Prediction

new_data = np.array([[2018, 8.5, 25000, 0, 1, 1]])  # Encoded input
scaled = scaler.transform(new_data)
price = model.predict(scaled)
print("Predicted Price:", price)

✅ Requirements

  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scikit-learn
  • joblib

Install them using:

pip install -r requirements.txt

📌 How to Use

  1. Clone the repo:

    git clone https://github.com/yourusername/Car-Price-Prediction.git

  2. Open Car_Price_Prediction.ipynb to train the model or understand the process.

  3. Use testing_on_newdata.ipynb to test the model on unseen data.

👨‍💻 Author

  • Venkata Sai Kumar Routhu