Introduction

In the previous chapter, we studied the different methods used to construct linear regression models. We learned how to use the least squares method to develop linear models. We made use of dummy variables to improve the performance of these linear models. We also performed linear regression analysis with a polynomial model to improve the model's performance. Next, we implemented the gradient descent algorithm, which handles large datasets and large numbers of variables with ease.

In this chapter, we will be developing autoregression models. Autoregression is a special type of regression that can be used to predict future values based on the experience of previous data in the set.