HousePrice_ML: An Efficient Framework for House Price Prediction Using Soft Computing | ||||
| Journal of Computing and Communication | ||||
| Article 8, Volume 3, Issue 1, January 2024, Page 104-115 PDF (860.55 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jocc.2024.339928 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Maged Farouk1; Nashwa Shaker1; Diaa s AbdElminaam   2; Omnia Elrashidy1; Mostafa Mahmoud3; Omar Mandour3; Hussien Walid3; Ali Mohamed3; Ibrahim Hossam3; Abdelrahman Ehab3; Reda Elazab1
| ||||
| 1Department of Business Information Systems, Faculty of Business, Alamein International University, Alamein, Egypt | ||||
| 2Department of Data Science , Faculty of Computer Science , Misr International University , Cairo , Egypt | ||||
| 3Department of Accounting and information System, Faculty of Business, Alamein International University, Alamein, Egypt | ||||
| Abstract | ||||
| Predicting housing prices is important to many people, such as home buyers, real estate agents, and investors. By harnessing the power of machine learning models, this paper aims to develop a highly efficient system to calculate reliable housing price forecasts. The results of this research can facilitate decision-making processes, enable more informed investments, and improve the overall buying and selling experience in the real estate market. The relationship between house prices and the economy is an important motivating factor for predicting house prices. This paper focuses on how to predict housing prices using machine learning techniques. This paper proposes an efficient framework For prediction houses using six machine learning algorithms ( SVM, Tree, Neural Network, KNN, Linear Regression, Gradient Boosting). In best model 1, the number of fields equals Gradient Boosting; in best model 2, the number of fields equals Linear Regression; in Best model 3 number of fields equals Gradient Boosting. The best all model equal model 2 equal Linear Regression. | ||||
| Keywords | ||||
| House price Prediction; Machine Learning; Artificial Intellgience; Linear Regression | ||||
| References | ||||
|
[1] Zhang, H., Li, Y., & Branco, P. (2023). Describe the house and I will tell you the price: House price prediction with textual description data. Natural Language Engineering, 1-35.
[2] Geerts, M., & De Weerdt, J. (2023). A Survey of Methods and Input Data Types for House Price Prediction. ISPRS International Journal of Geo-Information, 12(5), 200
[3] Murgia, A., Concas, G., Marchesi, M., & Tonelli, R. (2010, September). A machine learning approach for text categorization of fixing-issue commits on CVS. In Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement (pp. 1-10)
[4] Tang, W., Li, Z., & Cassar, N. (2019). Machine learning estimates of global marine nitrogen fixation. Journal of Geophysical Research: Biogeosciences, 124(3), 717-730.
[5] Dutta, S., Shi, A., & Misailovic, S. (2021, August). Flex: fixing flaky tests in machine learning projects by updating assertion bounds. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 603-614).
[6] 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.
[7] Tranmer, M., & Elliot, M. (2008). Multiple linear regression. The Cathie Marsh Centre for Census and Survey Research (CCSR), 5(5), 1-5.
[8] Abdi, H. (1994). A neural network primer. Journal of Biological Systems, 2(03), 247-281.
[9] Tellis, W. (1997). Application of a case study methodology. The qualitative report, 3(3), 1-19.
[10] Moschovakis, Y. N. (2001). What is an algorithm?. Mathematics unlimited—2001 and beyond, 919-936.
[11] Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21.
[12] Navada, A., Ansari, A. N., Patil, S., & Sonkamble, B. A. (2011, June). Overview of use of decision tree algorithms in machine learning. In 2011 IEEE control and system graduate research colloquium (pp. 37-42). IEEE.
[13] Li, X. (2013). Using" random forest" for classification and regression. Chinese Journal of Applied Entomology, 50(4), 1190-1197.
[14] Krämer, W., & Sonnberger, H. (2012). The linear regression model under test. Springer Science & Business Media.
[15] Jakkula, V. (2006). Tutorial on support vector machine (svm). School of EECS, Washington State University, 37(2.5), 3.
[16] Konečný, J., & Richtárik, P. (2017). Semi-stochastic gradient descent methods. Frontiers in Applied Mathematics and Statistics, 3, 9.
[17] Kuang, Q., & Zhao, L. (2009). A practical GPU based kNN algorithm. In Proceedings. The 2009 International Symposium on Computer Science and Computational Technology (ISCSCI 2009) (p. 151). Academy Publisher.
[18] Ying, C., Qi-Guang, M., Jia-Chen, L., & Lin, G. (2013). Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica, 39(6), 745-758.
[19] Manasse, M., McGeoch, L., & Sleator, D. (1988, January). Competitive algorithms for online problems. In Proceedings of the twentieth annual ACM symposium on Theory of computing (pp. 322-333).
[20] Banerjee, D., & Dutta, S. (2017, September). Predicting the housing price direction using machine learning techniques. In 2017 IEEE international conference on power, control, signals and instrumentation engineering (ICPCSI) (pp. 2998-3000). IEEE.
[21] Wang, P. Y., Chen, C. T., Su, J. W., Wang, T. Y., & Huang, S. H. (2021). Deep learning model for house price prediction using heterogeneous data analysis along with joint self-attention mechanism. IEEE Access, 9, 55244-55259. | ||||
|
Statistics Article View: 170 PDF Download: 115 |
||||
