Clustering Student Performance data Using Naive Bias Algorithm (Machine Learning)

Student dropout is a significant issue faced by educational institutions worldwide, impacting both individual learners and the broader educational system. Early identification of at-risk students can enable timely interventions to prevent dropout and improve student success rates. In this study, we propose a comprehensive approach to predict student dropout using machine learning techniques. Our methodology involves collecting and preprocessing various types of data, including demographic information, academic performance metrics, socio-economic factors, and behavioral data. Feature engineering techniques are employed to extract meaningful insights from raw data, enhancing the predictive power of our models. We then apply a variety of machine learning algorithms, such as logistic regression, decision trees, random forests, support vector machines, and neural networks, to train predictive models on the preprocessed data. By employing a diverse set of algorithms, we aim to explore different aspects of the data and capture complex patterns that may indicate dropout risk. To evaluate the performance of our models, we employ metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Additionally, we conduct cross-validation and hyperparameter tuning to ensure robustness and optimize model performance. The proposed approach is implemented and tested on a real-world dataset obtained from educational institutions, encompassing a diverse range of students across different academic levels and disciplines. Experimental results demonstrate promising predictive performance, with our models achieving high accuracy in identifying students at risk of dropout. Furthermore, we discuss the practical implications of our findings and highlight the importance of early intervention strategies in addressing student dropout. By leveraging machine learning techniques for dropout prediction, educational institutions can proactively identify and support at-risk students, ultimately fostering a more inclusive and successful learning environment.