https://towardsdatascience.com/5-ways-to-detect-outliers-that-every-data-scientist-should-know-python-code-70a54335a623

University of Michigan - Ann Arbor

Outlier: data that differ dramatically from all others and distinguish themselves in characteristics.

Method 1 — Standard Deviation: data point that is more than 3 times the standard deviation, then those points are very likely to be outliers

Method 2 — Boxplots: Any data points that show above or below the whiskers, can be considered outliers.

Method 3— DBScan Clustering: Three core concepts (points) in DBScan:
Core Points
Border Points
Everything else is called Noise Points

(https://towardsdatascience.com/5-ways-to-detect-outliers-that-every-data-scientist-should-know-python-code-70a54335a623)

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