Python code to select the best machine learning model using a trial and error approach.
In this code, the train_test_split() function from scikit-learn is used to split the dataset into training and testing sets. The for loop is used to iterate over the different models, and the model.fit() and model.predict() methods are used to train and evaluate each model. The accuracy_score() and f1_score() functions are used to calculate the accuracy and F1 score of each model. The best model and its performance are selected and printed at the end.