Abstract:
Purpose of this research is to investigate the accuracy of machine learning
models in forecasting and evaluating house prices, and to understand the key
factors that impact pricing. The study involved analyzing data scraped from
real estate ads in the “sale of secondary housing” category on the website
krisha.kz. The paper emphasizes the importance of understanding the factors
that affect house prices, such as quality, location, size, and building
materials. It was concluded that these factors have a strong correlation with
house price prediction. The information available on krisha.kz was found to
be a useful resource for finding good apartments. The data collected by the
scraper was analyzed by models: Linear regression (LR), interactions linear
regression (ILR), robust linear regression (RLR), fine tree regression (FTR),
medium tree regression (MTR), coarse tree regression (CTR), linear support
vector machine (LSVM), quadratic SVM (QSVM), medium gaussian SVM
(MGSVM), rational quadratic gaussian process regression (RQGPR),
boosted trees (BoosT), bagged trees (BagT), neural network based on the
bayesian regularization algorithm (BR-BPNN). BR-BPNN showed better
results than other models, with an MSE of 32.14 and R of 0.9899.
