Outlier Detection with Multivariate Normal Distribution in Python

Outlier Detection with Multivariate Normal Distribution in Python

Anything that is unusual and deviates from the standard “normal” is called an Anomaly or an Outlier. In this post, I will be using Multivariate Normal Distribution

_All the code files will be available at : [https://github.com/ashwinhprasad/Outliers-Detection/blob/master/Outliers.ipynb_](https://github.com/ashwinhprasad/Outliers-Detection/blob/master/Outliers.ipynb)

What is an Outlier ?

Anything that is unusual and deviates from the standard “normal” is called an Anomaly **or an Outlier.**

Detecting these anomalies in the given data is called as anomaly detection.

For more theoretical information about outlier or anomaly detection, Check out :** How Anomaly Detection Works ?**

Why do we need to remove outliers or detect them ?

*Case 1 : *Consider a situation where a big manufacturing company is manufacturing an airplane. An airplane has different parts and we don’t want any parts to behave in an unusual way. these unusual behaviours might be because of various reasons. we want to detect these parts before it is fixed in an airplane else the lives of the passengers might be in danger.

Image for post

*Case 2: *As you can see in the Above Image, how outliers can affect the equation of the line of best fit. So, before performing it is important to remove outliers in order to get the most accurate predictions.

In this post, I will be using Multivariate Normal Distribution

outlier-detection anomaly-detection machine-learning python outliers

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