Linear regression may be one of the most important algorithms in statistics, machine learning, and science in general. It's one of the most widely used algorithms, and it is very important to understand how to implement it and its various flavors. One of the advantages that linear regression has over many other algorithms is that it is very interpretable. We end up with a number (a coefficient) for each feature and such a number directly represents how that feature influences the target (the so-called dependent variable).
For instance, if you had to predict the selling value of a house and you obtained a dataset of historical sales comprising house characteristics (such as the lot size, indicators of the quality and condition ...