Why mahalanobis distance is incredibly powerful for outlier detection

Welcome to the thirteenth video of the series "Build your First Machine Learning Project". In this, we'll see how to detect Multi variate outliers with Mahalanobis Distance.
Notebook link: github.com/machinelearningplu...
The Mahalanobis distance is one of the most powerful distance measures in multivariate statistics.
It can be used to determine whether a sample is an outlier, whether a process is in control or whether a sample is a member of a group or not.
So let's understand it.
Chapters
0:00 Intro
2:42 What is Mahalanobis Distance
4:22 Difference between Euclidean and Mahalanobis Distance
8:02 Formula behind Mahalanobis Distance
12:11 Code behind Mahalanobis Distance
In order to make the best out of this, please watch this series in the order in playlist: Build Your First ML Model Playlist: • Build Your FIRST Machi...
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Previous Lesson:
How to Detect Outliers with Z Score : • How to Detect Outliers...
Earlier Lessons:
1. Build your first ML Project: • Build Your FIRST Machi...
2. How to Formulate ML Problem: • Build Your First ML Pr...
3. Setup Python Environment: • Setup Python Environme...
4. Jupyter Notebook Tutorial: • Jupyter Notebook Tutor...
5. What is ML Modeling: • What is ML Modeling? (...
6. Reduce the size of Pandas Dataframe: • Reduce the memory size...
7. What is EDA: • Exploratory Data Analy...
8. How to impute missing Data: • How to handle missing ...
9. Mice Imputation Algorithm: • Multiple Imputation by...
10. How to impute missing data in categorical Variables: • How to impute missing ...
11. Detect Outliers with IQR and Boxplot?: • How to Detect Outliers...
Let me know in the comments section if you have any questions!
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[@kevon217]

Great explanation!

[@machinelearningplus]

Thanks!

[@Ishqiyat]

loved it😍

[@machinelearningplus]

Thank you :)

[@ddrha770]

These are amazing and is very clear, please keep doing them!

[@machinelearningplus]

Sure!

[@machinelearningplus]

Notebook Link: github.com/machinelearningplus/Build-Your-First-ML-Project/tree/main/13b_Mahalanobis%20Distance%20for%20Multivariate%20Outlier%20Detection
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