Predict housing price using the method of combining multiple source data with mathematical model

Liu, Donghai; Liu, Long; Chen, Rundong; Tang, Shenrao; Hong, Siyi; Cui, Zegang

doi:10.47917/j.es.20190146

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2023

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“…This algorithm can make a reasonable estimate of the house price. However, it can be found that when using the linear regression algorithm, no matter how the learning rate is adjusted, the loss function will gradually converge to a big value [4,5]. This shows that there is an upper limit to the accuracy of the predicted value obtained by the linear regression algorithm.…”

Section: Introductionmentioning

confidence: 99%

Optimization of linear regression in house price prediction

Zhu

2023

ACE

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House price prediction plays a very important role in housing transactions. Linear regression based algorithms show good effects in predicting house prices. They have strong interpretability and fast operation speed. However, people ignore the estimation of deviations in linear regression (LR) algorithms. In this paper, k-nearest neighbor (KNN) algorithm is supposed to estimate deviations that are added to the result of linear regression to predict house prices accurately. Furthermore, deviation regression (DR) algorithm is supposed to make the prediction result more accurate. By utilizing Boston House Price data from Kaggle, extensive experiments are conducted and demonstrate the superior performance and compatibility of DR.

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Section: Introductionmentioning

confidence: 99%

Optimization of linear regression in house price prediction

Zhu

2023

ACE

View full text Add to dashboard Cite

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Predict housing price using the method of combining multiple source data with mathematical model

Cited by 1 publication

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Optimization of linear regression in house price prediction

Optimization of linear regression in house price prediction

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