Bengaluru House Price Prediction

Project Overview

This project builds a real estate price prediction website for Bengaluru using machine learning. It follows a step-by-step process:

Model Development: Trains a Linear Regression model on Bengaluru home price data from Kaggle.

Backend Development: Uses Python Flask to serve HTTP requests with the trained model.

Frontend Development: An interactive website built with HTML, CSS, and JavaScript.

Technologies Used

• Python for backend development and machine learning
• Flask for API server
• NumPy & Pandas for data processing
• Matplotlib for data visualization
• Scikit-learn for model training
• Jupyter Notebook for experimentation
• HTML/CSS/JavaScript for UI

Project Structure

BHP/
│── client/                 # Frontend (HTML, CSS, JS)
│── model/                  # Machine learning model files
│── server/                 # Backend API using Flask
│   │── artifacts/          # Saved model & column metadata
│   │── util/               # Utility functions
│   └── app.py              # Flask API entry point
│── bengaluru_house_prices.csv  # Dataset
│── README.md               # Project documentation

How to Run Locally

1. Clone the Repository

git clone https://github.com/your-username/BHP.git
cd BHP

2. Set Up a Virtual Environment (Optional but Recommended)

python -m venv venv
source venv/bin/activate  # Mac/Linux
venv\Scripts\activate     # Windows

3. Install Dependencies

pip install -r server/requirements.txt

4. Run the Flask Server

python server/app.py

The server will start at http://127.0.0.1:5000.

5. Run the Frontend

• Open client/index.html in your browser.
• Enter details (sqft, BHK, bath, location) and get predicted prices!

Features

1. Predicts house prices based on input parameters
2. Supports multiple locations in Bengaluru
3. Clean and interactive UI for easy predictions
4. API-based architecture for scalability

License

This project is open-source under the MIT License.