Overfitting means the model fitted to training data too well, in this case, we need to resample the data and estimate the model accuracy using techniques like k-fold cross-validation.
Whereas for the Underfitting case we are not able to understand or capture the patterns from the data, in this case, we need to change the algorithms, or we need to feed more data points to the model.
