Now that we know how to get the datasets that we need, how to quantify what we are trying to predict (objectives), and how to split data into training and testing datasets to evaluate our trained models on, let's dive into applying some basic machine learning techniques to our datasets:

• First, we will start with regression methods, which can be linear as well as non-linear.
• Ordinary Least Squares (OLS) is the most basic linear regression model, which is where we will start from.
• Then, we will look into Lasso and Ridge regression, which are extensions of OLS, but which include regularization and shrinkage features (we will discuss these aspects in more detail later).
• Elastic Net is a combination of both Lasso and Ridge regression methods. 
• Finally, our last regression method will be decision tree regression, which is capable of fitting non-linear models.