References
[1]. Truong, Q., Nguyen, M., Dang, H., & Mei, B. (2020). Housing price prediction via improved machine learning techniques. Procedia Computer Science, 174, 433-442.
[2]. Zulkifley N, Rahman S, Hasbiah U. House Price Prediction using a Machine Learning Model: A Survey of Literature. December 2020 International Journal of Modern Education and Computer Science 12(6):46-54.
[3]. Hjoirt A, Pensar J, Scheel I, Sommervoll D (2022). House Price Prediction with Gradient Boosted Trees Under Functions. JOURNAL OF PROPERTY RESEARCH2022, AHEAD OF PRINT, 1-27.
[4]. Kuvalekar, A., Manchewar, S., Mahadik, S., & Jawale, S. (2020, April). House Price Forecasting Using Machine Learning. In Proceedings of the 3rd International Conference on Advances in Science & Technology(ICAST).
[5]. Kaggle. House Prices–Advanced Regression Techniques. Data. https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data (accessed August 9, 2022)
[6]. Berrar, D. (2018, January). Cross-Validation. Reference Module in Life Sciences. doi:10.1016/B978-0-12-809633-8.20349-X
[7]. Scikit-Learn. Cross-validation: evaluating estimator performance. https://scikit-learn.org/stable/modules/cross_validation.html (accessed August 9, 2022)
[8]. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.
[9]. Microsoft. https://github.com/microsoft/LightGBM (accessed August 9, 2022).
[10]. Friedman, J. H. (2002). Stochastic gradient boosting. Computational statistics & data analysis, 38(4), 367-378.
[11]. Scikit-Learn. Gradient Boosting Regressor. https://scikit-learn.org/ sta- ble/modules/generated/sklearn.ensemble. GradientBoostingRegressor.html (accessed August 9, 2022).
[12]. Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 16 2016. doi:10.1145/2939672.2939785.
[13]. DMLC. xgboost. GitHub. https://github.com/dmlc/xgboost (accessed Au- gust 9, 2022).
Cite this article
Li,H. (2023). House price prediction based on machine learning. Applied and Computational Engineering,4,623-628.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Truong, Q., Nguyen, M., Dang, H., & Mei, B. (2020). Housing price prediction via improved machine learning techniques. Procedia Computer Science, 174, 433-442.
[2]. Zulkifley N, Rahman S, Hasbiah U. House Price Prediction using a Machine Learning Model: A Survey of Literature. December 2020 International Journal of Modern Education and Computer Science 12(6):46-54.
[3]. Hjoirt A, Pensar J, Scheel I, Sommervoll D (2022). House Price Prediction with Gradient Boosted Trees Under Functions. JOURNAL OF PROPERTY RESEARCH2022, AHEAD OF PRINT, 1-27.
[4]. Kuvalekar, A., Manchewar, S., Mahadik, S., & Jawale, S. (2020, April). House Price Forecasting Using Machine Learning. In Proceedings of the 3rd International Conference on Advances in Science & Technology(ICAST).
[5]. Kaggle. House Prices–Advanced Regression Techniques. Data. https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data (accessed August 9, 2022)
[6]. Berrar, D. (2018, January). Cross-Validation. Reference Module in Life Sciences. doi:10.1016/B978-0-12-809633-8.20349-X
[7]. Scikit-Learn. Cross-validation: evaluating estimator performance. https://scikit-learn.org/stable/modules/cross_validation.html (accessed August 9, 2022)
[8]. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.
[9]. Microsoft. https://github.com/microsoft/LightGBM (accessed August 9, 2022).
[10]. Friedman, J. H. (2002). Stochastic gradient boosting. Computational statistics & data analysis, 38(4), 367-378.
[11]. Scikit-Learn. Gradient Boosting Regressor. https://scikit-learn.org/ sta- ble/modules/generated/sklearn.ensemble. GradientBoostingRegressor.html (accessed August 9, 2022).
[12]. Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 16 2016. doi:10.1145/2939672.2939785.
[13]. DMLC. xgboost. GitHub. https://github.com/dmlc/xgboost (accessed Au- gust 9, 2022).