In this chapter, we talked about regression analysis, which is trying to fit a curve to a set of training data and then using it to predict new values. We saw its different forms. We looked at the concept of linear regression and its implementation in Python.

We learned what polynomial regression is, that is, using higher degree polynomials to create better, complex curves for multi-dimensional data. We also saw its implementation in Python.

We then talked about multivariate regression, which is a little bit more complicated. We saw how it is used when there are multiple factors affecting the data that we're predicting. We looked at an interesting example, which predicts the price of a car using Python and a very powerful tool, pandas.

Finally, we looked at the concept of multi-level models. We understood some of the challenges and how to think about them when you're putting them together. In the next chapter, we'll learn some machine learning techniques using Python.