Data mining is a growing domain. Wherever we are going, data mining Techniques are available to collect important information from the warehouse or from databases. In every sector, they need to keep data secure. Maintaining a balanced state of data is very crucial. Whenever a class tends to classify, the specimens in the class can be grouped as the majority group and the minority group. The majority group consists of higher number of data when compared with the data distributed in the minority group. This paper proposes the Randomized-Ensemble with Smote (RESMOTE) methodology to handle the class imbalance problem. This paper addresses imbalance issues and proposes a class imbalance solution using a sampling technique combined with certain classification metrics.