Also, it's critical to refrain from overfitting the prediction model with past data. As a result of learning noise or unimportant patterns from the training set, a modelsattae that performs well on training data but badly on fresh data is said to be overfitted. When training the prediction model, it's crucial to employ suitable methods like cross-validation and regularization to prevent overfitting. Finally, users need to exercise caution because the data used to train predictive models may contain biases.