AutoVall is a full-stack web application that allows users to estimate car prices by make, accidents, mileage, engine size, etc…, and save their desired predictions. Implemented a machine learning API with Flask, integrated it with a React frontend, and implemented user authentication (Spring Boot) and favorites using Supabase.
- ✅ Predicts prices for used cars based on features.
- ✅ User signup/login with JWT Authentication
- ✅ Handles various fuel types (Gas, Diesel, Electric, etc.)
- ✅ Accounts for accidents, mileage, age, and other key factors
- ✅ Save and manage desired predictions
- ✅ Supabase database integration
- ✅ CSV import for data entry viaKaggle)
- Backend: Java, Spring Boot, Python, Flask, Colab
- ML Framework/Tools: XGBoost, Pandas, Scikit-learn
- Frontend: Javascript, HTML, CSS, ReactJs
- Authorization JWT (Json Web Token)
- Database: Supabase
- Version Control: Git, GitHub
- Tools: Kaggle
| Method | Endpoint | Description |
|---|---|---|
| POST | /predict |
Make prediction |
- Clone the repository:
git clone https://github.com/yourusername/AutoVal.git
cd AutoVal/backend/flask_ML