House Price Prediction Based on Machine Learning Algorithms - Taking Ames as an Example

Abstract

This study delves into the significance and methods of predicting housing prices. Utilizing a dataset from Kaggle, the author selected 10 variables highly correlated with housing prices, including OverallQual, GrLivArea, and GarageCars. Various models such as random forest and multiple linear regression were employed for prediction and comparison. Results indicate that for data with strong linear relationships, the predictive performance of the multiple linear regression model surpasses that of the random forest model. The paper emphasizes the importance of data preprocessing on model accuracy and suggests that model selection should align with data characteristics and problem requirements. While providing a preliminary exploration of housing price prediction, the study acknowledges shortcomings such as incomplete variable selection and insufficient data processing, suggesting avenues for future research to address these limitations and enhance the predictive capabilities in this domain.