Abstract:

Machine learning (ML) has become the centralizing part of the modern scientific methodology, that offers an automated procedure for the prediction and classification of circumstances based on the unravelling, underlying patterns, and observations of data. It is a sub-field of artificial intelligence with the ability to learn and improve experiences from a given dataset without being explicitly programmed. Nowadays, as compared to the economy, industry, and aerospace applications, the utilization of ML models in real estate mainly in Malaysia is relatively few. Hence, this paper reports the finding of Systematics Literature Review (SLR) on the existing ML algorithms, which are commonly used for real estate modelling. Decision Tree (DT), Random Forest (FR), Ridge Regression (RR), and Lasso regression (LR) is the common algorithms described in this paper together with the training approaches (TA) and the performance metrics (PM).