Understanding Linear Regression in Python: A Practical Guide 👨‍🏫

Linear regression is one of the foundational techniques in the data scientist’s toolkit.

While it may sound intimidating at first, it’s actually quite straightforward and incredibly useful for making predictions. In this article, I’ll walk you through what linear regression is, why it matters, and how to implement it in Python.

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What is Linear Regression? 🤔

At its core, linear regression attempts to model the relationship between two variables by fitting a linear equation to the data. One variable is considered the explanatory variable (X), and the other is the dependent variable (y).

The equation for a simple linear regression looks like this:

y = mx + b

Where:

• y is the dependent variable we’re trying to predict
• x is the independent variable (our input)
• m is the slope of the line
• b is the y-intercept

The goal is to find the values of m and b that create a line minimizing the distance between all data points and the line itself.

Why Use Linear Regression? 🤔