What are some major mistakes in Data Science projects?
The question is about Data Science .
Common mistakes in Data Science projects include overfitting (where the model only performs well on training data and not on new data), data leakage (using information that should not be be included in the model), poor data quality, and unclear objectives. Careful quality control and a solid understanding of the business problem help prevent these issues.
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