Python

In these four lessons, you will discover how to build regression models. We will discuss what these are for shortly. But before you do anything, make sure you have the right tools in place to start the process!

In this lesson, you will learn how to:

• Configure your computer for local machine learning tasks.
• Work with Jupyter notebooks.
• Use Scikit-learn, including installation.
• Explore linear regression with a hands-on exercise.

R

There are many types of regression methods, and which one you pick depends on the answer you’re looking for. If you want to predict the probable height for a person of a given age, you’d use linear regression, as you’re seeking a numeric value. If you’re interested in discovering whether a type of cuisine should be considered vegan or not, you’re looking for a category assignment so you would use logistic regression. You’ll learn more about logistic regression later. Think a bit about some questions you can ask of data, and which of these methods would be more appropriate.

In this section, you will work with a small dataset about diabetes. Imagine that you wanted to test a treatment for diabetic patients. Machine Learning models might help you determine which patients would respond better to the treatment, based on combinations of variables. Even a very basic regression model, when visualized, might show information about variables that would help you organize your theoretical clinical trials.