🚗 Car Price Prediction System

An end-to-end Machine Learning web application that predicts the resale price of a car based on important vehicle features such as year, kilometers driven, fuel type, transmission, seller type, and ownership history.

This project combines a trained ML model with a Flask backend and a responsive front-end interface to deliver real-time price predictions.

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🌐 Live Demo

Deployed App:
https://car-price-prediction-vert.vercel.app/

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Project Preview

Feature Importance

Prediction Performance

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✨ Features

• Machine learning-based car price prediction
• Clean and responsive UI
• Real-time prediction output
• Feature engineering for better performance
• Input validation and error handling
• Flask backend integration
• Deployed on Vercel
• Supports both web form and model inference

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🧠 Machine Learning Workflow

Data Processing

• Data cleaning
• Missing value handling
• Label encoding
• Feature selection

Feature Engineering

The following engineered features were added to improve model performance:

• Car Age
• Kilometers Per Year

These help improve prediction quality.

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🛠️ Tech Stack

Frontend

• HTML5
• CSS3
• Bootstrap

Backend

• Python
• Flask

Machine Learning

• Pandas
• NumPy
• Scikit-learn

Deployment

• Vercel
• Gunicorn

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📂 Project Structure

car_price_prediction/
│
├── app/
│   ├── utils.py
│   ├── Car_Pred_Model.pkl
│   ├── Column.json
│   └── encoded_data.json
│
├── templates/
│   └── index.html
│
├── CONFIG.py
├── main.py
├── requirements.txt
├── vercel.json
├── .env
├── .gitignore
└── README.md

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⚙️ Installation and Setup

Clone the repository

git clone https://github.com/roshankodi/car-price-prediction.git
cd car-price-prediction

Create and activate a virtual environment

Windows:

python -m venv .venv
.venv\Scripts\activate

Mac/Linux:

python3 -m venv .venv
source .venv/bin/activate

Install dependencies

pip install -r requirements.txt

Run the application

python main.py

Open the app in your browser:

http://127.0.0.1:5000

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📊 Example Prediction

Input

Feature	Value
Year	2018
Kilometers Driven	45000
Fuel Type	Petrol
Seller Type	Dealer
Transmission	Manual
Owner	First Owner

Output

Predicted Price: ₹543,870.26

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🚀 Future Improvements

• Add model comparison dashboard
• Improve prediction accuracy
• Add visual analytics
• Add prediction confidence ranges
• Add user authentication
• Store prediction history

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👨‍💻 Author

Kodi Roshan

• GitHub: https://github.com/roshankodi
• LinkedIn: https://www.linkedin.com/in/kodi-roshan-78858b356
• Portfolio: https://roshankodi.github.io/portfolio-me/

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🙏 Credits

This project was built using open-source tools and libraries:

• Flask
• NumPy
• Pandas
• Scikit-learn
• Bootstrap

The machine learning workflow and regression approach were inspired by standard automobile resale prediction techniques.

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⭐ Support

If you found this project useful:

• Star the repository
• Fork the project
• Share feedback

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Made with Python, Machine Learning, and Flask 🚀