The forecasting of this research work implements the use of networks and data integration presenting the solution to analyze the each and individual performance implementing the algorithms of machine learning. Study mainly outlines the profuse objectives to represent and review the performance plethora in identification of students’ marks, cocurricular and extracurricular activities. It provides a casted layout in a definite display allowing the users to input the various data required as a part of academic curriculum. The dataset is comprehensive and more compact categorizing a real time features like marks and all other attributes taken by an institution. It is further processed in various models of classification including random forest, decision tree and other algorithms to obtain the score and its performance. The exploratory analysis of data along with evaluation of models in correspondence with feature engineering helps to cater with classification techniques to understand the performance of students in various levels. The live dashboard and an interactive system provide a definite outline to support practical usage and significant implementation in educational institutions. Statistical forecasting prevents the misleading of data keeping the clarity constant and supporting the efficacy of the system so that it supports as a tool of guidance in classification, generation and prediction of reports and results of students from the performance and gather the measures of feedback.
Ghosh et al. (Wed,) studied this question.
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