Your First Data Science Project: Is Your Model Score Too Good to Be True?

 A high model score looks great, but it only helps if the test resembles the problem you want to solve. Before adding another algorithm to a first data science project, check what information the model received and how the practice data was separated from the evaluation data.


Start with the moment the prediction must happen. If you predict whether an order will be returned when it is placed, a column such as refund processed is not known at that moment, so it should be excluded. Then keep training, validation and final test data separate, and do not keep changing the model to improve the test score.


Read the full beginner walkthrough on the ScholarLogic blog: https://scholarlogic.net/your-first-data-science-project-is-your-model-score-too-good-to-be-true/


ScholarLogic teaches Data Science at BTM Layout, Bengaluru, in classroom and live online batches.

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