codeflamer/Nigeria-Houses-Price-Prediction

# Nigeria Housing Price Prediction Project A comprehensive machine learning application for predicting house prices in Nigeria. This project consists of both a frontend web interface and a backend API service. ## Project Overview This application helps users predict house prices in Nigeria based on various property features such as location, number of rooms, and property type. The project is built with a modern architecture separating frontend and backend concerns. ## Project Structure ``` Nigeria Housing/ ├── App/ # Frontend Application │ ├── app.py # Gradio web interface │ ├── model/ # Model artifacts │ ├── images/ # State visualizations │ └── requirements.txt # Frontend dependencies │ ├── backend/ # Backend API Service │ ├── main.py # FastAPI application │ ├── util.py # Prediction utilities │ ├── artifacts/ # Model artifacts │ └── requirements.txt # Backend dependencies │ └── README.md # This file ``` ## Components ### 1. Frontend (App/) - Built with Gradio for a user-friendly interface - Features: - Interactive form for property details - Dynamic state and town selection - Visual representation of state data - Real-time price predictions - Responsive design ### 2. Backend (backend/) - RESTful API built with FastAPI - Features: - Price prediction endpoint - Health check endpoint - API documentation (Swagger/ReDoc) - Docker support - Model serving ## Getting Started ### Prerequisites - Python 3.9 or higher - Docker (optional, for containerized deployment) ### Running the Application #### Option 1: Run Frontend Only 1. Navigate to the App directory 2. Follow setup instructions in `App/README.md` #### Option 2: Run Backend Only 1. Navigate to the backend directory 2. Follow setup instructions in `backend/README.md` #### Option 3: Run Both Services 1. Start the backend service first 2. Start the frontend service 3. Access t …