Summary

This chapter marks the beginning of the introduction to the algorithms, which are the backbone of predictive modelling. These algorithms are converted into mathematical equations based on the historical data. These equations are the predictive models.

In this chapter, we discussed the simplest and the most widely used predictive modelling technique called linear regression.

Here is a list of things that we learned in this chapter:

• Linear regression assumes a linear relationship between an output variable and one or more predictor variables. The one with a single predictor variable is called a simple linear regression while the one with multiple variables is called multiple linear regression.

• The coefficients of the linear relationship (model) are estimated using the least sum of squares method.

• In Python, statsmodel.api and scikit-learn are the two methods to implement Python.

• The coefficient of determination, R², is a good way to gauge the efficiency of the model in explaining the error...