Our next machine learning model is a random forest classifier (RFC). An RFC is an ensemble classifier that builds multiple decision trees using a randomly chosen subset of the training dataset. Finally, unweighted voting from each decision tree is used to make predictions. The SearchCV algorithm in Scikit-learn program was used to optimize the hyperparameters. The optimized number of decision trees, minimum samples split, minimum samples leaf, and maximum depth are 1000, 2, 1, and 80, respectively.