Machine Learning for Placement Prediction: A Study Using Weka, Orange, and Simple ML

Abstract

Placement prediction is a crucial application of machine learning in education, helping institutions and students understand employability factors. It enables educational institutions to design effective training programs and assists students in improving their career prospects. This study evaluates the performance of three popular data analysis tools, namely Weka, Orange, and Simple ML, using a publicly available placement dataset. The dataset comprises various features, including academic performance, extracurricular involvement, and technical skills, which play a significant role in determining job placements. The models were assessed based on multiple evaluation metrics, including accuracy, precision, and recall. The findings offer valuable insights into the most effective tool for placement prediction, highlighting strengths, limitations, and potential areas for improvement. This research aims to assist educators, data scientists, and institutions in selecting the most suitable machine learning tool for predictive analytics in placement prediction.