House Price Forecasting Using Machine Learning Methods

Abstract

Machine learning has a huge impact in the previous years in picture recognition, spam redesign, typical discourse order, and item proposal. The current Machine learning calculation causes us to redesign security cautions, guaranteeing public wellbeing, and improving clinical improvement. By analyzing many algorithms, we decide to engage in machine learning. Determining the price of the house is vital these days as the price of the land and the price of the house increases each year. This utility can assist clients to make investments in a property except drawing near an agent. Here we use various regression methods of supervised machine learning using Python. Regression is used to predict future values based on the independent variable. The model’s evaluation is done by calculating the error value. When a small error occurred, it would give great accuracy to our regression model, so in this problem, we are going to predict the house values. The following algorithms Extreme Gradient Boosting, Gradient Boosting Regression, Random forest regression, Light Gradient Boosting Machine regression, support vector regression were used to forecast the house values. Further, these algorithms are compared according to the predicted results. The eventual outcome will be shown as the best calculation as far as forecast exactness.