Regression Training and Testing Practical Machine Learning Tutorial with Python p. 4

Welcome to part four of the Machine Learning with Python tutorial series. In the previous tutorials, we got our initial data, we transformed and manipulated it a bit to our liking, and then we began to define our features. ScikitLearn does not fundamentally need to work with Pandas and dataframes, I just prefer to do my datahandling with it, as it is fast and efficient. Instead, Scikitlearn actually fundamentally requires numpy arrays. Pandas dataframes can be easily converted to NumPy arrays, so it just so happens to work out for us It is a typical standard with machine learning in code to define X (capital x), as the features, and y (lowercase y) as the label that corresponds to the features. As such, we can define our features and labels like so.