Comparison Of Decision Tree, Linear Regression, and Random Forest Regressor Models for Predicting House Prices

Abstract

A home is a basic requirement that offers comfort and security to its occupants. Because they are subject to price fluctuations, houses are also a potential option in an investing setting. As a result, buyers and investors require a system that can forecast house values. This study compares the effectiveness of decision trees, linear regression, and random forest regressors as models for predicting home prices. The dataset for predicting home prices was used in this study to conduct data exploration, pre-processing, modeling, and model comparison stages. The study's findings demonstrate that the random forest regressor offers the best prediction performance with lower assessment metrics, including MAE, MSE, RMSE, and R2 Score, making it the best option for predicting house prices and other financial outcomes.