A Literature Review on Using Machine Learning Algorithm to Predict House Prices

Abstract

In this study, we use a variety of machine-learning methods to forecast the
sale prices of residences. The size, location, building type, age, number of
bedrooms, garages, and other characteristics of the property all affect how
much it is worth when it is sold. Machine-learning algorithms are employed to
develop the prediction model for houses in this article. Using machine learning
methods, such as call trees, supply regression, support vector regression, and
the Lasso Regression methodology, a prognostic model is developed in this
case. Also, we have contrasted supported parameters for these algorithms
such as MAE, MSE, RMSE, and accuracy. In this research, machine learning
algorithms are used as a hunting tool to create models for predicting housing
value.