Overfitting in machine learning happens when a model is so good at fitting the training data that it starts to memorize the noise or random variation present in the data, instead of learning the underlying pattern. This can lead to poor performance when the model is applied to new, unseen data. Imagine you are a store trying to predict the number of customers you will have in a day based on past data, like temperature and day of the week. You train a model on this data, and it seems to work great, it can predict the number of customers very accurately. But when you use the model to predict the number of customers for future days, it doesn't work as well because it's not accounting for other factors like holidays, special events, or unforeseen weather changes. This is similar to overfitting in machine learning, where a model is too good at fitting the training data, but doesn't generalize well to new, unseen data. In business problems, overfitting can lead to poor decision making and wasted resources. To prevent overfitting, we use techniques like cross-validation, regularization and early stopping.
