This paper aims to present the implementation of two machine learning algorithms, Linear Regression and Lasso Regression for the task of predicting the price of the house located in the city of Bengaluru, India. The task of predicting house price accurately is quite difficult as it depends on a lot of factors, and needs of the buyer. For predicting these prices, we can try to create datasets which contains details of the factors that people look for while buying Houses such as size, location, bathroom etc. To analyses the data, we can usecertain algorithms such as linear regression, lasso regression etc. By using algorithms like this, we can reduce the margin of error of our model and try to make it more accurate and usable. Such models can be used by real estate agents,sellers as well as the people who want to buy house to get the best deal for them. In future,researcher can also integrate models like this with the real estate's websites like MagicBricks,99Acres to give better and more accurate recommendations to people.

A comparative Analyis of House Price Prediction Algorithms