House Price Prediction Based on Machine Learning: A Case of King County

Abstract

Machine learning is commonly used in the real estate market. It is vital to apply the idea of machine learning in this field to predict house prices based on various features. The paper will focus on how to use the most appropriate machine learning models for house price prediction. It will use LightGBM(Light Gradient Boosting Machine), Gradient Boosting, and XGBoost(Extreme Gradient Boosting) to train models to predict house prices using the existing data from the Kaggle website. After three models make predictions, they will get an RMSE (root mean square error), whichis0.02975, 0.02537, and 0.01364. Based on the result, the XGBoost model is the best one among these three models used for house price prediction.