In the past, research have been carried out to study the behaviour of students as it is an important topic in psychology. Parents and teachers are concerned about their children's actions in class. Learning about students' behaviour in school is essential for teachers for their own development and growth of them. A class is composed of students with distinctive characteristics and capacities, where a few students are sharp and some are dull. In some cases, it becomes inconvenient for the educators to spot who is keeping the pace with them and who is falling behind. The proposed approach will allow the students to groom their personalities and overcome their shortcomings, and it will offer assistance to the instructor to identify which students require more consideration from them. To do that, we chose and applied the K-mean Clustering Algorithm. In other words, this research attempts to discover homogeneous subgroups inside the information. K means calculation is an iterative calculation that tries to tract the dataset into K pre-defined unmistakable non-overlapping subgroups (clusters), where each information point has a place as if it were one bunch. It tries to make the intra-cluster information focuses as comparable as conceivable, whereas also keeping the cluster as diverse (distant) as conceivable.