There are some basic assumptions of Linear Regression for which we must test our data in order to correctly apply Linear Regression. If these assumptions are being violated then we may obtain biased and misleading results.
In this blog, we will discuss these assumptions, in brief, using the ‘Advertising’ dataset, verify those assumptions and ways to overcome if these assumptions are being violated using Python.
Linear Regression is one of the important algorithms in Machine Learning. This algorithm is mainly used for regression problems. In one of our previous blog posts, the end to end implementation of this algorithm has already been presented using the ‘Boston dataset’. We assume our readers will have little basic knowledge of Linear Regression and its implementation. If not you can go through our previous blog to understand the implementation of Linear Regression in a detailed way.