Class imbalance is a common problem in machine learning where one class has significantly more data than another. This can lead to biased results. Researchers have gathered information from various sources to help users understand and address this issue. By increasing the number of samples in the minority class, better analysis can be achieved. This work aims to assist future researchers in tackling class imbalance problems effectively.