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- W4386985372 abstract "In recent times, there have been a surge in the housing business, such that prediction of houses is of utmost important both for the seller and the potential buyer. This has been influenced by several key indices. Many approaches have been used to tackle the issue of predicting house prices to help the house owners and real estate agents maximise their profit while the prospective buyers make better informed decision. This study focuses on building an effective model for the prediction of house prices. Since price is a continuous variable, it was expedient we used regression models. Some regression models like linear regression, Random Forest regressor (RF), Extreme Gradient Boosting Regressor (XGBoost), Support Vector Machine (SVM) regressor, K-Nearest Neighbor (KNN) and Linear regression were employed. The result showed that Random Forest Regressor showed a superior performance having an R2 score of 99.97% while SVM regressor performed poorly with an R2 score of −4.11%. The result proved that Random Forest regressor as an effective machine learning model to predicting house prices." @default.
- W4386985372 created "2023-09-24" @default.
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- W4386985372 date "2023-01-01" @default.
- W4386985372 modified "2023-10-01" @default.
- W4386985372 title "Effective House Price Prediction Using Machine Learning" @default.
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- W4386985372 doi "https://doi.org/10.1007/978-3-031-37164-6_32" @default.
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