Imbalanced data is often found in many types of data subject to machine learning. Imbalanced data can be found in various fields such as fraud detection, unauthorized network intrusion detection, failure detection, and medical diagnosis, and it is known that if there is an imbalance problem in data, it affects the learning stage and reduces the classification performance of the learning model. Techniques to alleviate the imbalance data problem include an under-sampling technique that matches a class with a high distribution of data by a low class and an over-sampling technique that matches a class with a low distribution by a high class. In order to improve the classification performance of imbalanced data, this study aims to determine whether the problem of imbalanced data can be alleviated by applying various data resampling techniques to various analysis methods and then comparing the classification performance. To this end, an oversampling technique and an undersampling technique that can alleviate the problem of unbalanced data were briefly introduced, and a case analysis was conducted using financial data provided by DACON to compare the performance of the data resampling technique according to various analysis methods.