Student Data Clustering using Machine Learning(Naïve Bayes Classifier & Decision Tree Algorithm)

G.Harshvardhan; S.Om Prakash

doi:10.36948/ijfmr.2024.v06i04.26244

Student Data Clustering using Machine Learning(Naïve Bayes Classifier & Decision Tree Algorithm)

Author(s)	G.Harshvardhan, S.Om Prakash
Country	India
Abstract	Student dropout is a significant issue in educational institutions worldwide, posing challenges to both students and educators. Early identification of students at risk of dropping out can enable timely interventions and support, potentially mitigating dropout rates. In this study, we propose a machine learning-based approach for predicting student dropout, leveraging a variety of student-related data. The data include demographic information, academic performance metrics, attendance records, socio-economic factors, and extracurricular activities. We preprocess the data by handling missing values, encoding categorical variables, and scaling numerical features. Feature selection techniques are employed to identify the most relevant predictors of dropout. Various classification algorithms, including logistic regression, decision trees, random forests, support vector machines, and neural networks, are evaluated for their predictive performance. The models are trained on historical data, split into training and testing sets, and evaluated using metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve (AUC-ROC). Hyperparameter tuning is performed to optimize the models' performance further. The results demonstrate the effectiveness of the proposed approach in accurately predicting student dropout. Additionally, we provide insights into the factors contributing most significantly to dropout risk, facilitating targeted interventions by educational institutions. This study contributes to the field of educational data mining by offering a practical framework for dropout prediction, thereby empowering educators to proactively address the issue of student attrition. Future research directions include the integration of additional data sources and the development of personalized intervention strategies based on predictive analytics.
Keywords	Student dropout prediction, Machine learning, Educational data mining, Predictive modeling, Early intervention, Academic success.
Field	Computer > Data / Information
Published In	Volume 6, Issue 4, July-August 2024
Published On	2024-08-31
Cite This	Student Data Clustering using Machine Learning(Naïve Bayes Classifier & Decision Tree Algorithm) - G.Harshvardhan, S.Om Prakash - IJFMR Volume 6, Issue 4, July-August 2024. DOI 10.36948/ijfmr.2024.v06i04.26244
DOI	https://doi.org/10.36948/ijfmr.2024.v06i04.26244
Short DOI	https://doi.org/gt9hd5

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Student Data Clustering using Machine Learning(Naïve Bayes Classifier & Decision Tree Algorithm)

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