Machine learning is used to teach machines how to handle the data more efciently. Simply, machine learning can be better understood as “learning from data” [13]. There are two types of machine learning algorithms, unsupervised and supervised [14]. Unsupervised learning uses machine learning algorithms (e.g., K-means, PCA) to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns in data without the need for human intervention (hence, they are “unsuper­vised”). It is mainly used for clustering, association, and dimensionality reduction. Supervised learning is a machine learning approach that’s dened by its use of labeled datasets. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately. Using labeled inputs and out­puts, the model can measure its accuracy and learn over time. Currently, supervised machine learning methods are primarily used in the ADA program, which includes Linear regression, Lasso, Decision trees, Random forest, and Gradient boosting. Lasso, Random forest, and Gradient boosting are chosen because of their feature selection properties. Linear regression and Decision trees are used as the linear and nonlinear benchmarks for model comparison. Other methods such as generalized random forest have been studied and applied as appropriate.