House Price Prediction - Bengaluru

Project Overview

This project aims to predict house prices in Bangalore using the Kaggle dataset Bengaluru_House_Data. Machine learning models such as Linear Regression, Lasso Regression, and Decision Tree were applied and compared. Detailed Exploratory Data Analysis (EDA) and feature engineering were performed to improve the predictive power.

Table of Contents

Project Overview Dataset Installation Project Structure Models Used Results Usage License Dataset The dataset used is Bengaluru_House_Data from Kaggle, which includes house price information in Bangalore.

Key features include:

Location Total Square Feet Number of Bathrooms Number of Bedrooms Price per Square Foot You can download the dataset here.

Installation To run this project, ensure you have Python 3.x installed. Install the required libraries with:

bash Copy code pip install pandas numpy scikit-learn matplotlib seaborn Project Structure The project is organized as follows:

Models Used

The following models were utilized and compared in terms of performance:

Linear Regression: A simple regression model that assumes a linear relationship between dependent and independent variables. Lasso Regression: An extension of linear regression that applies L1 regularization to prevent overfitting. Decision Tree: A non-linear model that captures more complex relationships within the data.

Results

Linear Regression outperformed the other models, providing the most accurate predictions. Lasso Regression performed similarly but added regularization to mitigate overfitting. Decision Tree captured non-linear relationships but slightly underperformed compared to linear models. Key insights from EDA and feature engineering significantly improved the prediction accuracy.

License

This project is licensed under the MIT License - see the LICENSE file for details