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This article will explain in detail how to manage our modeling project file in big data. The content of the article is of high quality, so the editor shares it for you as a reference. I hope you will have some understanding of the relevant knowledge after reading this article.

You can first take a look at this picture, which is the demo of a project file recommended by individuals when we are doing a modeling project.

The file structure of this project is often used by me, and I will delete some content according to the complexity of the project, but the overall framework is similar, so share it with you for reference, because it is quite good to use personally. The picture is still more detailed, but I will pick some key points to briefly explain:

Experiment: dedicated to storing our experimental files, that is, intermediate files that constantly test the algorithm. Model: the folder where the final version codes of different algorithms are stored data: the folder where the data is stored, such as external (data from third parties), interim (data sources that have been partially cleaned and transformed, such as SQL, SAS), and raw (original dataset). Do not add any processing), processed (the final data set for modeling), code (for storing data cleaning code) on how to manage our modeling project file in big data is shared here, I hope the above content can be of some help to you, you can learn more knowledge. If you think the article is good, you can share it for more people to see.

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