Random forest (RF) is an ensemble of decision trees that are fitted on various subsamples of the data and uses averaging to restrict overfitting and improve prediction accuracy. The 'RandomForestClassifier' method from Scikit-learn was used to build the model. The number of estimators was set to 300 and random state was set to an integer. The rest of the parameters were set to default values.