The Decision Tree model leverages a tree-like structure, splitting the data into smaller sections at each tree node and using the average of values within each section to predict an output . The as determined by a Euclidean distance, to form a prediction . The Support Vector Regression (SVR) model identifies a hyperplane that maximizes the margin between the training data points and a specified margin of tolerance, aiming to fit as many data points within the margin while minimizing the error beyond it . The Artificial Neural Network (ANN) model processes input data through interconnected nodes, connections, and layer, where they undergo weighted transformations and nonlinear activations, to achieve an output prediction .