Machine Learning Tool to Predict Student Categories After Outlier Removal

Abstract

Statistical outlier detection techniques uses academic performance oriented results to find the truly brilliant as well as the weakest amongst a colony of students. Machine Learning allows further partitions within the remaining student community, based on both merit and personality. Present work proposes a decision tree model for predicting three more appropriate categories. It utilizes Text Analytic tools to assess student characteristic traits from their textual responses and feedbacks. The cream of the general pool is chosen to belong to a top class comprising the mentor group, provided they can academically assist the weaker of the lot. But all on the top may not be suited for mentor-ship role - textual assessment data delves to reveal character orientations favouring such decisions. The bulk who can manage their own forms the second class. The bottom of the pool benefits with assistance from the mentor group and comprise the third class.