House Price Prediction Using Linear and Lasso Regression

This research paper aims to predict house prices with more efficiency and accuracy. The research paper involves three machine learning algorithms, "linear regression, lasso regression, and ridge regression," that are applied to the data set of Bangalore and have various information about different places in Bangalore, like BHK, number of bathrooms, area, etc. The house price prediction is quite a difficult task, as predicting the price of the house involves many factors like area, number of bathrooms, climate condition, age of the property, and many other factors. To make predictions according to all the factors, the author has applied three algorithms, one by one. From these machine learning algorithms, we have developed a model that helps predict the prices of houses with more accuracy and a lower error percentage. This model can be very useful for both buyers and sellers, as it can justify the price of the house for both of them.