This document describes a project to predict house prices in Bengaluru, India using machine learning. It outlines the dataset used, data cleaning and feature engineering steps, outlier detection and removal processes, model creation using linear regression, and validation techniques. The final model achieved 87% accuracy in predicting house prices based on features like location, area, number of bedrooms and bathrooms. A function was also created to allow users to input these features and get a predicted price output.