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.