https://towardsdatascience.com/5-ways-to-detect-outliers-that-every-data-scientist-should-know-python-code-70a54335a623
https://towardsdatascience.com/dbscan-algorithm-complete-guide-and-application-with-python-scikit-learn-d690cbae4c5d

University of Michigan - Ann Arbor

- Isolation forest
- Robust random cut forest 
- Boxplots/Statistical Approach


Ways of detecting outliers

Isolation forests isolate outliers found using anomaly detection. This is adverse to other techniques that typically use data profiling (which are statistical methods used to describe the data set as a whole).


Isolation  Forest

Robust random cut forests also isolate outliers, however they do so through association. It associates a score to an anomaly, thereby analyzing these scores on a basis of “normal”, “low” or “high”. It works extremely well on robust data. 


Robust random cut forests 


There are many statistical approaches besides boxplots that include (but are not limited to): z-score, standard deviation, etc.
Box Plots create a visual of the spread of data. It automatically marks what it all,analyzes as an anomaly.


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