C2C – Predictive Analysis of Student Campus Placement

C2C - Predictive Analysis of Student Campus Placement

Prof. A.M. Chadchankar1, Kajol Pawar2, Vilas Dasalkar3, Nihar Jachak4, Prasad Kurale5

1Assistant Professor, Department of Computer Engineering, NBNSTIC, Pune, Maharashtra, India

2345Student, Department of Computer Engineering, NBNSTIC, Pune, Maharashtra, India

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Abstract - Student placement holds undeniable significance in academic institutions, influencing admissions and overall university prestige. Universities strive to enhance their placement services, and this study focuses on analyzing recent student data to predict placement potential. The advanced predictive algorithm aims to optimize university effectiveness in student placement, benefiting both students and the institution. The methodology relies on a meticulously preprocessed dataset from the same institution, showing a significant increase in accuracy compared to established classification algorithms. The campus placement prediction system employs educational data mining and parameters from college records and tests, utilizing logistic regression for accurate predictions. The model enhances academic performance and soft skills, outperforming traditional algorithms and providing valuable insights through predictive analytics. This study offers a comprehensive perspective on predictive analytics in campus placement prediction.

Key Words :  Data Mining, Placement, Logistic Regression, KNN, Supervised Machine Learning, Accuracy, Prediction, Classification, Decision Tree, Random Forest, Data Analysis, Predictive Analytics, SVM, XGBoost.