I was wondering if I have the correct order of preprocessing/EDA/feature engineering below?

Yes there are nuances and may vary from problem to problem, but am just looking for a general pipeline for 90% of machine learning problems I will encounter:

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1 Answer 1


I think you will find an answer to your questions in this paper:

Biswas, S., Wardat, M., & Rajan, H. (2022, May). The art and practice of data science pipelines: A comprehensive study of data science pipelines in theory, in-the-small, and in-the-large. In Proceedings of the 44th International Conference on Software Engineering (pp. 2091-2103).

The authors analysed many different data science pipelines (among others 21 best GitHub projects in this area). They extracted and coined a few proposals for standards, I'm sure you will find something for yourself. For instance - you can read what are the distributions of order of stages between each other, how often some stages are used in general or what is the most popular pipeline.

  • $\begingroup$ Thank you, just read the paper. However, it does not seem like it dives deeply into the Data Preparation stage, which is what I am looking for (proper order of operations in EDA/Data Preparation/Feature Engineering stage) , especially in the Large Models section of the paper (which is most representative of the real world practical data science pipeline). $\endgroup$
    – Katsu
    Nov 28, 2023 at 22:48

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