if __name__ == "__main__":
    
    # load, split steps
    ...
    
    # create experiment
    my_exp = mlflow.set_experiment("sklearn_experiment")
    
    # start MLflow run
    with mlflow.start_run(experiment_id=my_exp.experiment_id):
    
        # Train model
        ...
        
        # Log model hyperparameters
        mlflow.log_params({"n_estimators": n_estimators,
                           "max_depth": max_depth})
        
        # Log performance metrics
        mlflow.log_metrics({"F1 score": f1,
                            "Accuracy": accuracy,
                            "Precision": precision})
        
        # log model                    
        mlflow.sklearn.log_model(rf_model, "model")