Bengaluru House Price Prediction

Project Overview

This project focuses on predicting real estate prices in various locations across Bengaluru based on features such as location, square footage, number of bedrooms, and number of bathrooms. The objective is to provide a practical solution that estimates property prices using historical data and machine learning techniques.

Objective

The goal of this project is to build a regression model that can accurately predict the market value of residential properties in Bengaluru. The model is intended to help buyers, sellers, and real estate agents make informed decisions based on data-driven insights.

Tools and Technologies

• Programming Language: Python
  
  

• Libraries and Frameworks: Pandas, NumPy, Matplotlib, Scikit-learn
  
  

• Machine Learning Model: Linear Regression
  
  

• Web Technologies: HTML, CSS, JavaScript
  
  

• Backend Framework: Flask
  
  

• Deployment: Render
  
  

Data and Preprocessing

The dataset consisted of various property listings from Bengaluru. Key preprocessing steps included handling missing values, feature engineering, converting categorical variables using one-hot encoding, and removing outliers. The data was then normalized and prepared for training the regression model.

Model Development

A Linear Regression model was trained on the cleaned dataset to learn the relationship between input features and property prices. The model was evaluated using metrics such as R-squared and Mean Absolute Error. Feature selection techniques were applied to retain only the most impactful variables.

Web Interface

The project includes a fully functional web interface where users can input property details and receive real-time price predictions. The frontend was developed using HTML, CSS, and JavaScript, while the backend was implemented in Flask to serve the machine learning model. The complete application was deployed on Render for public access.

Project Significance

This project highlights the integration of data analysis, machine learning, and full-stack web development. It provides a practical example of how predictive modeling can support decision-making in the real estate sector.