To make administrative decisions and deliver high-quality education, it is essential to analyze student academic performance in educational institutions. The amount of information relating to educational institutions is growing quickly. The management will be able to make academic decisions with the use of machine learning from these vast volumes of data. Predicting a student's academic success early on in their course will assist academia in identifying the merit students and in concentrating more attention on creating remedial programmes for the poorer students to boost their performance. This also helps in their placements. Placements are a very crucial point for all academic institutions. In this paper, the K Means clustering technique is used for categorization of students’ data.