title: The development of house price forecast model based on machine learning
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
Creators: name: Ð§Ð¶Ð°Ð¾ Ð¡Ð¸Ð½ÑÑÐ¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð· ÑÐµÐ½ Ð½Ð° Ð¶Ð¸Ð»ÑÐµ
Ð¼Ð¸ÐºÑÐ¾ÑÐ°ÐºÑÐ¾ÑÑ
ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð½Ð¾Ð¶ÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð¹ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸
house price forecast
micro factors
multiple regression models comparison

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description: The given work is devoted to build multiple house price models based on machine learning. The research set the following goals: Summarize the current state of development of machine learning and the current state of research on house price prediction. Pre-processing of Hangzhou second-hand house price data and analysis of the influence on house price of house features. Construct six popular regression models by using Python: linear regression, Lasso regression, ridge regression, random forest, decision tree, support vector machine and K-Nearest Neighbor model. Adjust modelâs parameters to obtain the best prediction result of each model. Compare the results of all models , find out and analyse the model with the best prediction results. The study result revealed that in the Hangzhou second-house price empirical analysis, the linear regression model was not fitted with high precision and was less accurate. While the random forest model performed the best, R2 reached to 0.606 andÂ  RMSE is 0.088. This study offers some guidance to house buyers to select the second-house. And it can assist housing developer to make a reasonable investment decision. Besides,it will also help the government to develop relevant policies and regulations to control house prices.