Chapter 5. Linear Regression with Python
If you have mastered the content of the last two chapters, implementing predictive models will be a cake walk. Remember the 80-20% split between the data cleaning + wrangling and modelling? Then what is the need of dedicating a full chapter to illustrate the model? The reason is not about running a predictive model; it is about understanding the mathematics (algorithms) that goes behind the ready-made methods which we will be using to implement these algorithms. It is about interpreting the swathe of results these models spew after the model implementation and making sense of them in the context. Thus, it is of utmost importance to understand the mathematics behind the algorithms and the result parameters of these models.
With this chapter onwards, we will deal with one predictive modelling algorithm in each chapter. In this chapter, we will discuss a technique called linear regression. It is the most basic and generic technique to create a predictive...
