To improve the predictive performance and interpretability of our machine learning model, we employed the Least Absolute Shrinkage and Selection Operator (LASSO) regularization technique, which applies L1 regularization to penalize and shrink less important feature coefficients towards zero. In order to evaluate the model, we perform five-fold cross-validation, which checks that our models generalize well and do not overfit the training data. One-fold was reserved for the validation set for each iteration, and the remaining folds served as the training set.