• Jyoti Bhanwase
• S. S. Palnitkar
• Gunjan Gahukar
• Tushar Budhe

Every academic enthusiast's main goal is to get placed in a reputable MNC, and even an institute's reputation and annual admittance depends on the placement it delivers to its students. As a result, any system that predicts student placements will have a favourable influence on an institute, increasing strength and reducing workload in the training and placement office (TPO). Machine Learning techniques can be used to extract knowledge from previously placed students and predict the placement of future students. The data for training comes from the same institute that does the placement prediction. Along with the feature selections, appropriate data pre-processing procedures are used. Some domain knowledge is utilized. We employed Logistic, SVM, KNN, Decision Tree, Random Forest, advanced techniques like Bagging and Boosting to reach 74 percent in AdaBoost Classifier.

Pre-processing, Feature Selection, Domain expertise, Outliers, Bagging, Boosting, SVM, KNN, Logistics

"Placement Prediction Model Using Ensemble Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.556-559, August-2022, Available :http://www.jetir.org/papers/JETIRFP06099.pdf

"Placement Prediction Model Using Ensemble Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. pp556-559, August-2022, Available at : http://www.jetir.org/papers/JETIRFP06099.pdf