CAMPUS PLACEMENTS PREDICTION & ANALYSIS USING MACHINE LEARNING

Abstract

Placement of students stands as a paramount objective for educational institutions, as it profoundly influences an institution's reputation and annual admissions. Recognizing this pivotal role, institutions tirelessly endeavor to fortify their placement departments to enhance their overall standing. Enhancements in this area not only benefit the institution but also contribute positively to students' prospects. In this study, we aim to analyze data from previous years' students to predict the placement chances of current students. A predictive model, integrated with an algorithm tailored for this purpose, is proposed. Data collected from the same institution underwent suitable preprocessing methods. Furthermore, our model's efficacy was compared with traditional classification algorithms such as Decision Trees and Random Forests, focusing on metrics like accuracy, precision, and recall. Results indicate that our proposed algorithm outperforms the aforementioned algorithms significantly.