Developing Strategic Interventions in Enhancing English Language Proficiency of Engineering Students Using Deep Forest and Python

This paper explores how Deep Forest can be leveraged in the process of prediction of correlations across concord, prepositions, articles, tenses, vocabulary and speaking and fortification of English language Skills. A study was conducted with 50 first-year Engineering students to examine their proficiency levels in the domains of concord, prepositions, articles, tenses, vocabulary and speaking on a scale of 10. The data was processed to predict the correlations and to understand student’s proficiency level. The data was further processed to classify students based on their scores into different levels. The study suggests that data analytical tools and techniques can be used by the educational institutions and teachers to plan strategic interventions synchronous with the objectives of the course.