Building Models with Machine Learning

In the following section we will guide you through building simple machine learning models. At times, media and pop culture makes "Machine Learning" sound as if you are building a rocket to move to Mars - however, most of you likely already have come across machine learning in the past: a simple linear regression, as you can see in the picture below, already is a machine learning algorithm.

Source: Mathworks

While machine learning covers supervised and unsupervised learning, for our project we will stick to the supervised learning algorithms in order to predict outcomes. How do we expect you to complete the exercises? We will introduce you to a simple linear regression model (you may find that useful in future courses!), as well as to a decision tree model. While we do not expect of you to explain to us the mathematics behing a regression model, we do want you to understand what you are doing to a degree where you are able to explain your algorithm. That said: we want you to still comment your code extensively in all the following exercises. Furthermore, when completing the decision tree exercise, we want you to explain the parameters which you are setting and what they do. Take the questions we ask of you as guidance, however, the main goal for you should be to explain what your code & algorithm does. You should also understand and explain the outputs you get, whether they are statistically significant is secondary for the scope of this project in learning code.