Bengaluru House Price Prediction

Get instant price estimates for properties in Bengaluru.

🚀 Live Demo

Click here to use the deployed app!

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Project Overview

This project builds a machine learning model to predict house prices in Bangalore based on factors like location, square footage, and the number of rooms. The model is deployed as a web app using Flask, HTML, and CSS, and is accessible online.

Dataset

• Dataset used: Bengaluru_House_Data.xls
• Location: Bangalore
• Features:
  • Total square footage
  • BHK (bedrooms, hall, kitchen)
  • Bathrooms
  • Location
• Target: House price (in Lakhs)

Requirements

Install the necessary packages with the following command:

pip install -r requirements.txt

Usage

Local Usage

1. Open the Notebook: Launch the Jupyter Notebook and open Bengaluru house price.ipynb.
2. Run the Cells: Execute each cell step-by-step to load data, preprocess it, train the model, and evaluate performance.
3. Predict Prices: Use the trained model to predict prices by running the final cell.
4. Run the Web App Locally:
   • Make sure you have model.pkl and columns.pkl generated from the notebook.
   • Run:

     python app.py
     

   • Open http://127.0.0.1:5000/ in your browser.

Online Usage

• Use the live app here: https://bengaluru-house-price-prediction-acly.onrender.com

Deployment

This app is deployed for free on Render:

• All code and assets are in this repository.
• The Procfile and requirements.txt are set up for easy deployment.
• Static files (including background image) are in the static/ folder.

Technologies Used

• Python
• Flask for the web app
• Pandas and NumPy for data manipulation
• Scikit-Learn for model building and evaluation
• Bootstrap and custom CSS for UI

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© 2025 Bengaluru House Price Predictor