Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/15622
Title: Improving Predictive Analytics for Student Dropout: A Comprehensive Analysis and Model Evaluation
Authors: Sabbir, Wahid
Abdullah-Al-Kafi, Md
Afridi, Arafat Sahin
Rahman, Md Sadekur
Karmakar, Mousumi
Keywords: Predictive Analytics
Random Forest
XGBoost
ROC Curve
Data Preprocessing
Class Imbalance
Issue Date: 2024
Publisher: 11th International Conference on Computing for Sustainable Global Development, INDIACom 2024
Institute of Electrical and Electronics Engineers Inc.
Citation: pp. 951-956
Abstract: This research project uses careful data preparation and machine learning model assessment to provide an in-depth analysis of a dataset of students in college or university. The first analysis looks at goal value distributions, economic variables, and student counts by gender. The handling of outliers, feature selection, and class imbalance are all addressed by further filtering. Using ROC curves to highlight classification strength, the study assesses several classifiers, including XGBoost, Random Forest, K-Nearest Neighbors (KNN), and Decision Tree. With the greatest AUC of 0.99, Random Forest remarkably shows excellent predictive power, closely followed by XGBoost at 0.98. XGBoost performs exceptionally well on testing and training datasets. The findings contribute valuable insights into predictive modeling for understanding and predicting student outcomes, emphasizing the potential to enhance educational support systems. This integrated approach, combining exploratory data analysis and machine learning techniques, establishes a robust framework for future research in educational data mining and predictive analytics. © 2024 Bharati Vidyapeeth, New Delhi.
URI: http://dx.doi.org/10.23919/INDIACom61295.2024.10498520
http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/15622
ISBN: 9789380544519
9798350394504
Appears in Collections:Conference Papers

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