Crucial steps in Cluster Analysis everyone should know

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After seeing and working a lot with clustering approaches and analysis I would like to share with you four common mistakes in cluster analysis and how to avoid them.

Mistake #1: Lack of an exhaustive Exploratory Data Analysis (EDA) and digestible Data Cleaning

The use of the usual methods like .describe() and .isnull().sum() is a very good way to start an exploratory analysis but should definitely not be the end of your EDA. A deeper (visual) analysis of the variables and how they correlate with each other are essential. Otherwise…

Konstantin Rink

Data Scientist working on Customer Insights💡

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