A Comparative Machine Learning Method for Predicting Campus Placement using Academic and Skill-Based Features
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A Comparative Machine Learning Method for Predicting Campus Placement using Academic and Skill-Based Features
Dr. K. Satyam1, Konda Swetha2
1Associate Professor, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra Pradesh, India.
2 Post Graduate, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra Pradesh, India.
Abstract:Campus placement has a big impact on students' careers, yet predicting placement outcomes is still challenging due to the influence of many academic and skill-based factors. This work provides a machine learning-based approach to predict student placement status using structured data. The proposed system makes use of features like academic performance (SSC, HSC, MCA), programming skills, internships, projects, certificates, and participation in hackathons. Three supervised learning algorithms—Random Forest, Decision Tree, and Logistic Regression—are put into practice and evaluated. Experimental results show that Random Forest and Decision Tree models are more accurate than Logistic Regression. The technology is further integrated into an internet application to provide forecasts in real time. This tactic can improve employability and give institutions and students a better understanding of placement trends.
Keywords:Campus Placement Prediction, Machine Learning, Decision Tree, Random Forest, Logistic Regression, Classification
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