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This paper comprehensively analyzes data mining techniques and performance metrics applied to a logistic regression model. Principal Component Analysis (PCA) was utilized to diminish the complexity of high-dimensional data, enabling clearer visualization and examination of intricate relationships among variables. The logistic regression model demonstrated commendable performance on both test and train sets, as evidenced by high values of accuracy, precision, recall, ROC AUC, and F1 Score were observed. The provided confusion matrices offered detailed insights into the model's accuracy in classifying positive and negative instances. Concerning our hypotheses, we found no significant relationship between gender and academic performance, supported by a highly significant p-value of 0.0 and a weak positive correlation coefficient of 0.0847. However, we noticed a strong positive correlation of 0.99 between gender and exam characteristics, although it has not reached statistical significance for a p-value of 0.281. Our research contributes valuable insights into data analysis, model evaluation, and the interplay between variables. The findings can inform decision-making in real-world applications and warrant further investigation of identified relationships to enhance practical implications. Future studies should consider exploring additional factors, such as the subject of study, semester, and study year, to further understand their impact on student performance.
Selim et al. (Tue,) studied this question.