House Price Prediction Based on Machine Learning --Taking Beijing House Price Data as an Example

House price prediction has significant practical value in the real estate market, as it provides critical decision support for specific needs of buyers, sellers, and investors. However, accurate forecasting remains challenging due to the complex and multi-factorial nature of housing prices. This study investigates the application of machine learning algorithms for house price forecasting to enhance predictive accuracy. To achieve this aim, we examine several classical machine learning models including decision trees, linear regression, k-nearest neighbors, random forests, AdaBoost, Bagging, gradient boosting, and Extra Tree. Using housing price data from Beijing containing attributes such as transaction time, age of the house, and the average community price, we employ feature engineering to process the data.