Utilizing Machine Learning for Predictive Analytics in Career Path Forecasting

Abstract

Despite the presence of excellent educational institutions in India, the country still faces a high student dropout rate. This can be attributed to various factors, primarily being the selection of an inappropriate career path. To tackle this concern, it is crucial to offer students appropriate guidance, so that they can make informed decisions about their future careers which are aligned with their preferences and interests. This will help in avoiding future complications like discontentment, poor performance, fear and stress, social neglect etc. Machine Learning (ML) paves the path for students by making predictions regarding the future career path selection, with the help of application of various algorithms like Decision Tree (DT)[1], Random Forest (RF)[2], Support Vector Machine (SVM)[3], k Nearest Neighbor (kNN)[4], Naïve Bayes (NB) [5], Adaboost [6] and Logistic Regression (LR).

Therefore, the objective of this paper is four folds. Initially, to carry out an in-depth literature review emphasizing the application of ML techniques in predictive analysis. Top of Form Secondly, the paper compares and contrasts the ML techniques most suitable and apt for students’ choosing their career option. Thirdly, the paper applies these ML techniques on a dataset and evaluates them against different parameters like accuracy, precision and recall. Finally, the paper concludes while analyzing the results.