Housing Price Prediction Application

This repository contains a housing price prediction application built with a React frontend and a Flask backend. The application allows users to input various features of a house and predict its price using different machine learning algorithms.

Table of Contents

• Overview
• Features
• Technologies Used
• Installation
• Usage
• Project Structure
• Contributing

Overview

The Housing Price Prediction Application provides an interface for users to enter details about a property and choose a machine learning algorithm to predict the price of the property. The backend is equipped with multiple models, including XGBoost, Linear Regression, Random Forest, and Gradient Boosting.

Features

• User-friendly interface to input house features
• Dropdown menus for categorical features
• Selection of various machine learning algorithms
• Display of predicted price and model performance metrics (R² scores)
• Real-time prediction using a Flask API

Technologies Used

Frontend

• React

Backend

• Flask
• Pandas
• Scikit-learn
• XGBoost
• Flask-CORS

Installation

Follow these steps to set up the application locally.

Frontend

1. Clone the repository:

   git clone https://github.com/your-username/housing-price-prediction.git
   cd housing-price-prediction/frontend

2. Install dependencies:

   npm install

3. Start the React application:

   npm start

Backend

1. Navigate to the backend directory:

   cd ../backend

2. Set up a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate   # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Place the dataset (dataset_etiketlenmis_son.xlsx) in the backend directory.

5. Start the Flask application:

   flask run

Usage

1. Open the frontend application in your browser at http://localhost:3000.
2. Fill in the form with the necessary house details.
3. Select a machine learning algorithm from the dropdown menu.
4. Click the "Gönder" button to get the price prediction.
5. View the predicted price and model performance metrics displayed on the page.

Project Structure

housing-price-prediction/
├── frontend/
│   ├── src/
│   │   ├── MainPage.js
│   │   ├── dataset.js
│   │   └── ...
│   ├── public/
│   └── package.json
└── backend/
    ├── main.py
    ├── requirements.txt
    └── dataset_etiketlenmis_son.xlsx

Frontend

• MainPage.js: The main component containing the form and logic for handling user input and API requests.
• dataset.js: Contains dataset mappings for categorical feature transformations.

Backend

• main.py: The Flask application with endpoints for model training and prediction.
• requirements.txt: Lists the Python dependencies for the backend.

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Make your changes and commit them:

   git commit -m "Add new feature"

4. Push to the branch:

   git push origin feature-branch

5. Submit a pull request.