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What are some common mistakes to avoid when building models

Sure! Here are four common mistakes to avoid when building models:

**Ignoring Data Quality**: One big mistake is not paying enough attention to the quality of your data. If your data is messy, incomplete, or biased, your model's predictions will be off. Always clean and preprocess your data before diving into modeling.

**Overfitting**: A lot of folks get caught up in making their model super complex to fit the training data perfectly. But this can backfire—your model might perform great on the training set but bomb on new data. Keep it simple and use techniques like cross-validation to check how well it generalizes.

**Neglecting Feature Engineering**: Sometimes, people skip the step of feature engineering, thinking that the model will figure everything out. But the right features can make a huge difference! Spend time selecting and transforming features that really capture the essence of the problem.