Car_Price_Prediction_Using_Linear-Regression

🚗 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