House Price Prediction: Comparative Analysis of Regression-Based Machine Learning Algorithms

Abstract

Abstract: Advancement in technology has revolutionized the ways of doing things in contemporary time. One of such is Artificial intelligence which has given birth to myriads of methods/techniques employed in solving real life problems. Many Machine learning techniques can be used in predicting house prices with several factors in consideration. House prices rise on annual basis, triggering the need for house price prediction models. Predictive models enable families to acquire a house of their choice when accurately developed. There have been a significant number of articles that adopt traditional machine learning algorithms to successfully estimate house prices, but they rarely compare the performance of individual models. This study will extensively test and compare numerous machine learning technique and present an optimistic model that will be used in developing a house pricing prediction system. Several models were developed and compared using Linear Regression (LR), Least Absolute Shrinkage & Selection Operator Regression (LASSO-R), Ridge Regression (RR), K-Nearest Neighbours Regression (KNN-R), Decision Tree Regression (DTR) and Extra Trees Regression (ETR) algorithms. Implemented using Python programming language. Amongst them ETR outperformed the others with MSE (16233.4), RMSE (128.7), MAE (49.6) and R2 (0.63) while the least performed is KNN-R with MSE (45763.3), RMSE (213.9), MAE (99.2) and R2 (-0.04).