Housing Price Prediction Based on Multiple Linear Regression | Zendy

Qingqi Zhang | Zendy

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Housing Price Prediction Based on Multiple Linear Regression

Author(s) -

Qingqi Zhang

Publication year - 2021

Publication title -

scientific programming

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.269

H-Index - 36

eISSN - 1875-919X

pISSN - 1058-9244

DOI - 10.1155/2021/7678931

Subject(s) - linear regression , real estate , computer science , econometrics , regression analysis , proper linear model , set (abstract data type) , regression , data set , test set , test (biology) , correlation coefficient , bayesian multivariate linear regression , statistics , machine learning , artificial intelligence , economics , mathematics , finance , paleontology , biology , programming language

In this paper, the author first analyzes the major factors affecting housing prices with Spearman correlation coefficient, selects significant factors influencing general housing prices, and conducts a combined analysis algorithm. Then, the author establishes a multiple linear regression model for housing price prediction and applies the data set of real estate prices in Boston to test the method. Through the data analysis and test in this paper, it can be summarized that the multiple linear regression model can effectively predict and analyze the housing price to some extent, while the algorithm can still be improved through more advanced machine learning methods.

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