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, Pages 104-115 PDF (860.55 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/jocc.2024.339928 | ||
| 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 | ||
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