What are some common mistakes in machine learning projects?

What are some common mistakes in machine learning projects?

Some common mistakes include not defining the problem clearly, using insufficient data, and neglecting data preprocessing.

Can you elaborate on the importance of data preprocessing?

Data preprocessing is crucial as it helps in cleaning and transforming raw data into a format suitable for modeling, improving accuracy.

What about model selection? Any common pitfalls?

Yes, a common pitfall is choosing a complex model without understanding the data, which can lead to overfitting.

How can I avoid overfitting in my models?

You can avoid overfitting by using techniques like cross-validation, regularization, and simplifying your model.