Walkthrough: Simple Linear Regression

By clicking on this link it hopefully means that you’re interested in machine learning and would like to know how to implement your own machine learning models. If so, you’ve come to the right place. I’m going to publish a series of posts demonstrating how to programme popular supervised and unsupervised machine learning models.

This series is aimed at those who care less about what’s going on ‘under the hood’ and are focused on getting results quickly, with little effort. I’m going to publish my code along the way so you can steal…I mean, repurpose it for your own usage.

Python is my programming language of choice and I’ll be using Jupyter Notebooks to annotate the code in easy-to-follow-along steps. To keep this series consistent I’m going to be using only a handful of Python libraries: pandas for data manipulation and analysis, NumPy for mathematical functions, scikit-learn to provide the machine learning algorithms and seaborn and/or matplotlib for visualisations.

As this is the first post in this series I’m going to start with the simplest of models — the simple linear regression.