Utilizing a highly complex model with an excessive number of features without considering the risk of overfitting, despite having a large dataset.

Following industry best practices meticulously to ensure the model's robustness and generalizability, including cross-validation and feature selection.

Collecting a dataset that, while large, is not representative enough of the problem space, leading to underfitting because it lacks diversity in customer behaviors and demographics.

Applying advanced regularization techniques to prevent the model from learning noise in the training data, without first ensuring the dataset's representativeness.