The understudy's execution expectation is an imperative research theme since it can enable teachers to keep understudies from dropping out before final tests and distinguish understudies that need extra help. So Accurately foreseeing understudies' future execution dependent on their progressing scholarly records is vital for successfully completing important instructive intercessions to guarantee understudies' on-time and palatable graduation. With the help of machine learning technology, using decision tree algorithm the academic performance and extra-curricular activities of a student will be monitored and learnt to provide alert to the respective tutor. Based on this alert sent by the automated system, the performance of a student could be improved effectively.
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