Learn how to prepare the data for machine learning algorithms and how to fine tune the hyper-parameters of a model
This lesson covers the basics of how and when to perform data preprocessing. This essential step in any machine learning project is when we get our data ready for modeling. Between importing and cleaning the data and fitting the machine learning model is when preprocessing comes into play.
We'll learn how to standardize the data so that it's in the right form for the model, create new features to best leverage the information in the dataset, and select the best features to improve the model fit.
Building powerful machine learning models depends heavily on the set of hyperparameters used. But with increasingly complex models with lots of options, how do we efficiently find the best settings for our particular problem?
In this lesson we will get practical experience in using some common methodologies for automated hyperparameter tuning in Python using Scikit Learn. These include Grid Search & Random Search.
We will use a dataset predicting credit card defaults as we build skills to dramatically increase the efficiency and effectiveness of our machine learning model building.