Real Estate Price Prediction based on Supervised Machine Learning Scenarios

Abstract

House price prediction is one of the most common supervised learning tasks in the machine learning field, which makes it a perfect criterion for the effectiveness of different learning models. From basic regression models to neural networks, countless methods have been proposed to solve the house price prediction problem. In this paper, the focus is the performance of three regression models, linear, LASSO, and ridge. There will be a selected dataset of sold houses from the open-source website. The data will be explored and visualized for a better understanding and then implement the regression models for further testing. According to the analysis, the LASSO regression model can yield the most accurate prediction with 90.15% accuracy but need a specific λ value. The linear and ridge regression yields similar predictions with close to 90% accuracy. Therefore, the most effective model for the house price prediction problem is the LASSO regression model. Overall, these results shed light on guiding further exploration of the performance of different machine learning models.