What should I do if there are too many zero values in the outlier handling part?

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I am working on a data science project which is about Churn analysis(whether costomer is leaving or not). I am trying to do outlier handling part but i have a question about how i need to think when my data has many zero values. I know it may contain a meaning but please see the results below. Results ,Value Counts, z score-hard edges and outliers

I would like to ask what should i need to do for better results and should i keep all the zero values? Any suggestion? What should I do if there are too many zero values in the outlier handling part?

data-science outliers machine-learning python
1 Answers

This question is too broad to be asked here. Stackoverflow is mainly for programming questions, I recommend you to post your question on stats or data-science as your question would have more potential to be answered in a broader way.

I guess 0 values are not missing as @yatu suspected, inferred from the colname, it means no change in revenue. Moreover, 0s are not the outlier values.

Refer to this similar discussion.

I can suggest one more reading, but the paper would convey the intuition where it does not discuss your problem explicitly. Yet, you may find it useful. Of course, do not forget to search for references.

Further reading: A Statistical Model for Big Data with Excessive Zero-Inflated Problem

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