Open Access
Issue
E3S Web Conf.
Volume 418, 2023
African Cities Conference (ACC 2023): A part of African Cities Lab 2023 Summit
Article Number 03001
Number of page(s) 6
Section Emerging Technologies and Applications to African Cities Issues
DOI https://doi.org/10.1051/e3sconf/202341803001
Published online 18 August 2023
  1. Alfiyatin, A. N., Febrita, R. E., Taufiq, H., & Mahmudy, W. F. (2017). Modeling house price prediction using regression analysis and particle swarm optimization case study: Malang, East Java, Indonesia. International Journal of Advanced Computer Science and Applications, 8(10). [Google Scholar]
  2. Kang, Y., Zhang, F., Peng, W., Gao, S., Rao, J., Duarte, F., & Ratti, C. (2021). Understanding house price appreciation using multi-source big geo-data and machine learning. Land Use Policy, 111, 104919. [CrossRef] [Google Scholar]
  3. Greenaway-McGrevy, R., & Sorensen, K. (2021). A Time-Varying Hedonic Approach to quantifying the effects of loss aversion on house prices. Economic Modelling, 99, 105491. [CrossRef] [Google Scholar]
  4. Filip, F. G., Zamfirescu, C. B., & Ciurea, C. (2017). Computer-supported collaborative decisionmaking. Cham: Springer International Publishing. [CrossRef] [Google Scholar]
  5. Kayode, A. A., Akande, N. O., Adegun, A. A., & Adebiyi, M. O. (2019). An automated mammogram classification system using modified support vector machine. Medical Devices: Evidence and Research, 275-284. [CrossRef] [Google Scholar]
  6. Aderonke, K., Oluwatobi, A., Jabaru, S., & Tinuke, O. (2020). An Empirical Investigation of the Prevalence of Osteoarthritis in South West Nigeria: A PopulationBased Study. [Google Scholar]
  7. Noah Akande, O., Christiana Abikoye, O., Anthonia Kayode, A., & Lamari, Y. (2020). Implementation of a framework for healthy and diabetic retinopathy retinal image recognition. Scientifica, 2020. [Google Scholar]
  8. Tékouabou Koumétio, S. C., & Toulni, H. (2021). Improving knn model for direct marketing prediction in smart cities. In Machine Intelligence and Data Analytics for Sustainable Future Smart Cities (pp. 107-118). Cham: Springer International Publishing. [CrossRef] [Google Scholar]
  9. Tékouabou, S. C., Gherghina, Ş. C., Toulni, H., Mata, P. N., & Martins, J. M. (2022). Towards Explainable Machine Learning for Bank Churn Prediction Using Data Balancing and Ensemble-Based Methods. Mathematics, 10(14), 2379. [CrossRef] [Google Scholar]
  10. Shinde, N., & Gawande, K. (2018). Valuation of house prices using predictive techniques. Journal of Advances in Electronics Computer Science, 5(6), 34-40. [Google Scholar]
  11. Dagar, A., & Kapoor, S. (2020). A Comparative Study on House Price Prediction. International Journal for Modern Trends in Science and Technology, 6(12), 103-107. [Google Scholar]
  12. Jha, S. B., Pandey, V., Jha, R. K., & Babiceanu, R. F. (2020). Machine learning approaches to real estate market prediction problem: a case study. arXiv preprint arXiv:2008.09922. [Google Scholar]
  13. Hjort, A., Pensar, J., Scheel, I., & Sommervoll, D. E. (2022). House price prediction with gradient boosted trees under different loss functions. Journal of Property Research, 39(4), 338-364. [CrossRef] [Google Scholar]
  14. Ho, W. K., Tang, B. S., & Wong, S. W. (2021). Predicting property prices with machine learning algorithms. Journal of Property Research, 38(1), 48-70. [CrossRef] [Google Scholar]
  15. Zou, C. (2023). The House Price Prediction Using Machine Learning Algorithm: The Case of Jinan, China. Highlights in Science, Engineering and Technology, 39, 327-333. [CrossRef] [Google Scholar]
  16. Tékouabou, S. C. K., Chabbar, I., Toulni, H., Cherif, W., & Silkan, H. (2022). Optimizing the early glaucoma detection from visual fields by combining preprocessing techniques and ensemble classifier with selection strategies. Expert Systems with Applications, 189, 115975. [CrossRef] [Google Scholar]
  17. Glen, S. (2020, December 28). Absolute Error & Mean Absolute Error (MAE). Statistics How To. Retrieved from https://www.statisticshowto.com/absolute-error/ [Google Scholar]

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