Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/15622
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dc.contributor.authorSabbir, Wahid-
dc.contributor.authorAbdullah-Al-Kafi, Md-
dc.contributor.authorAfridi, Arafat Sahin-
dc.contributor.authorRahman, Md Sadekur-
dc.contributor.authorKarmakar, Mousumi-
dc.date.accessioned2024-05-29T08:51:24Z-
dc.date.available2024-05-29T08:51:24Z-
dc.date.issued2024-
dc.identifier.citationpp. 951-956en_US
dc.identifier.isbn9789380544519-
dc.identifier.isbn9798350394504-
dc.identifier.urihttp://dx.doi.org/10.23919/INDIACom61295.2024.10498520-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/15622-
dc.description.abstractThis 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.en_US
dc.language.isoenen_US
dc.publisher11th International Conference on Computing for Sustainable Global Development, INDIACom 2024en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.subjectPredictive Analyticsen_US
dc.subjectRandom Foresten_US
dc.subjectXGBoosten_US
dc.subjectROC Curveen_US
dc.subjectData Preprocessingen_US
dc.subjectClass Imbalanceen_US
dc.titleImproving Predictive Analytics for Student Dropout: A Comprehensive Analysis and Model Evaluationen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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