Overfitting is a common issue in machine learning and data analysis where a model learns the details and noise in the training data to an extent that it performs poorly on new, unseen data. This happens because the model becomes too complex and starts to capture random fluctuations and outliers in the training data instead of the actual underlying patterns.
