A Comprehensive Analysis in Predicting Student Success based on School Academic Records using ZeroR and Logistic Regression Algorithms
Keywords:
Prediction, Forecasting, Machine Learning, Logistic Regression, ZeroR AlgorithmAbstract
Student success is intricately woven with various factors, including exam scores, class participation, and organizational experiences. This research embarks on a captivating exploration, delving into the multifaceted elements that shape student success based on meticulous scrutiny of school records. This study aims to analyze the determinants of student success based on school records. The examination of academic factors facilitates the prediction of success in both academic and non-academic domains. By analyzing students' academic factors, we can predict student success in academic and non-academic achievements in the future. Because, basically, student success is not only based on academic achievement, but also in non-academic fields. This research compares the effectiveness of the ZeroR and Logistic Regression methods.Leveraging academic records from schools across diverse nations, we navigate through a tapestry of attributes to discern patterns. Logistic Regression proves more adept at discerning nuanced patterns within the data, resulting in more precise predictions. Notably, the Logistic Regression algorithm demonstrated an accuracy of 73.75%, markedly surpassing ZeroR, which achieved an accuracy of 43.96%. This substantial difference underscores the superior performance of Logistic Regression in predictive analysis. In conclusion, the limited predictive capacity of the ZeroR algorithm, ethered to its limitation in predicting a singular class, highlights the necessity of employing advanced models like Logistic Regression for precise student success predictions based on school records.
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