TY - GEN
T1 - Effective House Price Prediction Using Machine Learning
AU - Zhou, Jincheng
AU - Hai, Tao
AU - Maxwell-Chigozie, Ezinne C.
AU - Adedayo, Afolake
AU - Chen, Ying
AU - Iwendi, Celestine
AU - Boulouard, Zakaria
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - House price prediction
KW - Machine learning
KW - Regression algorithm
UR - https://www.scopus.com/pages/publications/85174509022
U2 - 10.1007/978-3-031-37164-6_32
DO - 10.1007/978-3-031-37164-6_32
M3 - Conference contribution
AN - SCOPUS:85174509022
SN - 9783031371639
T3 - Lecture Notes in Networks and Systems
SP - 425
EP - 436
BT - Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions
A2 - Iwendi, Celestine
A2 - Boulouard, Zakaria
A2 - Kryvinska, Natalia
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023
Y2 - 24 February 2023 through 25 February 2023
ER -