A University or educational institute generally receives a bulk of complaints posted by students every day. The issues relate to their academics or any issues relates to their education or related to exam sections etc., because of these bulk of complaints received from the students every day makes it difficult for the university to sort out them and classify them and send to their respective department for resolving the issues. In this project, we work on classifying these complaints based on the classes or departments they belong to, using. By using TF-IDF (term frequency-inverse document frequency) it finds terms which are more related to a specific document by converting to vectors. By capturing some keywords in the complaints, adding some weight to the keywords and using different Machine Learning classification‘s we are classifying the complaint based on these keywords. This classification makes the works easier for the university and saves time which is used to sort them and gives better service for the students. Now they can directly send the complaints to the respective departments with ease.