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- W4226079604 abstract "Each student dreams of the placement offer letter in their hand before concluding their final year of Engineering and Technology. Even the reputation of every institute depends on the placement that they provide to their students. In this research, Machine Learning (ML) techniques are applied to the dataset of previously placed students to predict the placement of upcoming batch students. In this work, we followed the Cross-Industry Standard Process (CRISP) methodology with the help of ML model building processes such as Feature selection, Label encoding, Feature scaling, Normalization, and Standardization. We have selected the ML models for the prediction of the placement by experimenting and comparing suit of ML classification algorithms using the K-fold cross-validation method and Ensemble Learning (EL) ML method. The suit of ML algorithms covers Logistic Regression, K-Nearest Neighbors’, Decision Tree Classifier, Random Forest Classifier, Naive Bayes and Support Vector Machine classifiers. Under EL, we have tested Adaptive Boosting, Extreme Gradient Boosting (XGBoost), and Grid Search CV methods. EL methods are advanced and popular, and hence it gives the best performance on a predictive modeling project. The performance XGBoost algorithm is best in predicting students’ placement in the early stage than that of different algorithms employed in the study with the support of relevant input features. In the end, researchers have suggested “A free guide to notify the campus placement status (FGNCPS)” web module through which placement aspirant students will get to know the placement status in advance and as per prediction, unplaced students get time to improve their weaker areas." @default.
- W4226079604 created "2022-05-05" @default.
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- W4226079604 date "2022-01-01" @default.
- W4226079604 modified "2023-09-27" @default.
- W4226079604 title "Predictive Analytics Model of an Engineering and Technology Campus Placement" @default.
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- W4226079604 doi "https://doi.org/10.1007/978-981-19-0863-7_21" @default.
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