Is it possible to manage an imbalanced dataset? If yes, how?

This is probably one of the toughest machine learning interview questions in data scientist interviews. The imbalanced dataset is found in cases of classification test and allocation of 90% of data in one class. As a result, you can encounter problems. Without any predictive power over the other data categories, the accuracy of around 90% could skew. However, it is possible to manage an imbalanced dataset.

You can try collecting more data to compensate for the imbalances in the dataset. You could also try re-sampling of the dataset in order to correct imbalances. Most important of all, you could try another completely different algorithm on the dataset. The important factor here is the understanding of the negative impacts of an imbalanced dataset and approaches for balancing the irregularities.