Simple and sophisticated methods are often under-valued when trying to solve complex problems. This story is intended to show how linear regression is still very relevant, and how we can improve the performance of these algorithms, and become better machine learning and data science engineers.

As a fresher in the field of machine learning, the first thing that you learn would be simple univariate linear regression. However, for the past decade or so, tree-based algorithms and neural networks have overshadowed the significance of linear regression on a commercial scale. The purpose of this blog post is to highlight why linear regression and other linear algorithms are still very relevant and how you can improve the performance of such rudimentary models to compete with large and sophisticated algorithms like XGBoost and Random Forests.

#regression #artificial-intelligence #machine-learning #data-science