House Price Prediction API

A FastAPI-powered REST API for predicting house prices using a trained machine learning model.

Features

• Predict price for a single house (/predict)
• Predict prices for multiple houses (/predict_batch)
• Model training and evaluation in Jupyter notebook
• API testing notebook

Getting Started

1. Clone the repository

git clone https://github.com/your-username/House-Prediction-API.git
cd House-Prediction-API

2. Install dependencies

pip install -r requirements.txt

3. Run the API

uvicorn src.main:app --reload

4. Test the API

• Use testing/test.ipynb to send requests and view predictions.

Repository Structure

House-Prediction-API/
│
├── notebooks/
│   ├── model.ipynb         # Jupyter notebook for model development
│   └── model.pkl           # Trained model file
│
├── src/
│   ├── main.py             # FastAPI application
│   └── __pycache__/        # Python cache files (can be ignored)
│
├── testing/
│   └── test.ipynb          # Notebook for API testing
│
├── README.md               # Project overview and instructions
├── requirements.txt        # Python dependencies
└── .gitignore              # Files/folders to ignore in git

File Overview

• notebooks/model.ipynb: Model development and training.
• notebooks/model.pkl: Saved model for inference.
• src/main.py: FastAPI app serving prediction endpoints.
• testing/test.ipynb: Example requests to the API.
• requirements.txt: List of required Python packages.

License

MIT