Comparative Analysis of Machine Learning Performance in House Price Prediction for Ames Iowa

Abstract

The price of housing is closely related to the quality of people's lives, which is reflected in various aspects (e.g., finding a place to live and managing their finances). On this basis, being able to predict house prices would be a great convenience, and this study examines the accuracy of multiple mechanistic learning regression models in predicting future house price changes. To be specific, this paper downloaded the dataset from Kaggle which includes the training and testing csv files. and ran many different regression techniques including lasso and more models on google Colab. Furthermore, a comparison between the results of different methods is carried out by estimating the mean square error. Then, this research combined some of the best yielded results of the model to combine into a final model and got a result with a high score. According to the analysis and evaluations, the study concluded that the hybrid model resulted in a more accurate prediction than the single model. These results shed light on guiding further exploration of real estate prediction.