Machine Learning Algorithms from Start to Finish in Python: Linear Regression. Probably one of the most common algorithms around, Linear Regression is a must know for Machine Learning Practitioners.

Probably one of the most common algorithms around, Linear Regression is a must know for Machine Learning Practitioners. This is usually a beginner’s first exposure to a real Machine Learning algorithm, and knowing how it operates on a deeper level is crucial to gain a better understanding of it.

So, briefly, let’s break down the real question; What *really* is **Linear** **Regression**?

Linear Regression is a supervised learning algorithm that aims at taking a linear approach at modelling the relation between a dependent variable and an independent variable. In other words, It aims to fit a linear *trendline _that best captures the _relationship* of the data, and, from this line, it can predict what the target values may be.

Great, I know the definition, but how does it *really* work? Great question! In order to answer the question, let’s run through a step by step process of how Linear Regression really operates:

- A trendline is fit to the data(as illustrated above).
- The distance between the points(the red dots on the figure being the points, and the green line being the distance) is calculated, and then squared, before they are summed(The values are squared to ensure that negative values do not produce an incorrect value and hinder the calculation). This is the error of the algorithm, or better knows as the
*residual* - The residual for the iteration is stored
- Based on an _optimisation algorithm, _thelineis slightly “shifted” so that the line may fit the data better.
- Steps 2–5 are repeated until a desirable result is reached, or the residual error has decreased to zero.

This method of fitting a line is known as *Least Squares.*

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