Home Price Prediction API

A Flask-based web application for predicting home prices based on location, square footage, number of bedrooms, and bathrooms.

Overview

This project provides a web interface and API for real estate price predictions. The application uses a machine learning model (managed by the util module) to estimate property prices based on various features.

Features

• Web interface for home price prediction
• REST API endpoints for programmatic access
• Location-based price estimation
• Considers square footage, bedrooms, and bathrooms

Installation

1. Clone the repository:

   git clone https://github.com/bhushanzade02/HOUSE-PRICE-DS.git
   cd HOUSE-PRICE-DS
   

2. Create virtual environment

   python -m venv .venv
   ./.venv/Scripts/activate
   

3. Install required dependencies:

   pip install -r requirements.txt
   

4. Start the server:

   python server/server.py
   

API Endpoints

GET /

• Returns the home page

GET /get_location_names

• Returns a list of available locations
• Response format: {"locations": ["location1", "location2", ...]}

POST /predict_home_price

• Predicts the price of a home based on input parameters
• Request body format (JSON):

  {
    "total_sqft": 1000,
    "location": "Example Location",
    "bhk": 2,
    "bath": 2
  }

• Response format: {"estimated_price": 1500000}

Usage

1. Access the web interface by navigating to http://localhost:5000 in your browser
2. Select a location, enter the square footage, number of bedrooms, and bathrooms
3. Submit the form to get a price estimate

Development

• Server runs in debug mode by default
• CORS headers are enabled for all API endpoints
• Error handling is implemented for the prediction endpoint

Starting the Application

python server/server.py

The server will start with the message "Starting Python Flask Server For Home Price Prediction..." and will be accessible at http://localhost:5000.