What are the common pitfalls in implementing predictive analytics models, and how can they be avoided?

Common pitfalls include overfitting, data leakage, and inadequate feature selection. These can be avoided by using proper validation techniques, cross-validation, and ensuring a clean dataset.For example, Using k-fold cross-validation to ensure the model generalizes well.