Implementing Linear Regression in Python

A Simple Guide for Beginners

Image by Concord90 from Pixabay

Let’s say that you want to do more with your data. You know that predications and forecasting are possible, but where do you begin? PowerBI and Tableau have fine tools for these purposes, but you don’t really understand what they are doing. If you learn even a little bit of machine learning you now have powerful knowledge that help you find the key insights hidden in your data. But where to even begin with that?

The easiest machine learning model to learn is typically considered to be Linear Regression.

Here’s why:

1. Simplicity and Interpretability:

Linear Regression is straightforward and intuitive because it is based on the idea of fitting a straight line (or hyperplane in higher dimensions) to the data. The model attempts to find the best-fitting line that minimizes the difference between the predicted values and the actual data points. The relationship it models is simple: the dependent variable is a linear combination of the independent variables.

2. Minimal Computational Requirements:

Unlike more complex models such as deep learning or decision trees, linear regression doesn’t require a lot of computational resources. The…