# Fair and Transparent Student Admission Prediction Using Machine Learning Models
## 4 Results
### Experiments for MBA Dataset
Specifically:  
* Precision indicates the model's effectiveness in minimizing false positives, critical for decision-making tasks where incorrect classifications can have significant consequences. * Kappa reflects a high level of agreement between the predicted and actual classifications beyond random chance, underscoring the model's reliability. * MCC, though moderate, provides a more nuanced measure of correlation between predictions and actual outcomes, considering class imbalance. * These attributes make Naive Bayes particularly effective in applications where the cost of false positives is high, such as screening processes where the precision of classification outweighs the need for broader recall. Logistic Regression, on the other hand, excels in metrics that emphasize a balanced and inclusive approach, including accuracy (0.8468), recall (0.9722), and F1-score (0.9154):
* High recall indicates the model's ability to identify nearly all true positives, minimizing false negatives--a critical requirement in contexts where inclusivity and sensitivity are paramount. * F1-score, which harmonizes precision and recall, underscores the model's robust overall performance, balancing both the reduction of false positives and false negatives. * Accuracy further supports its ability to deliver consistent, reliable classifications across the dataset. These characteristics position Logistic Regression as an excellent choice for tasks where fairness and inclusivity are critical, such as admissions systems. By effectively balancing precision and recall, it ensures that opportunities are distributed equitably while minimizing the risk of overlooking qualified candidates. Figure 1 presents the feature importance plot produced by the Logistic Regression model applied to the MBA Dataset. This plot illustrates the relative contributions of individual features to the model's predictive performance, shedding light on their impact and significance. By visualizing these contributions, we gain valuable insights into the factors which are most influential in shaping the model's outcomes. Such an analysis not only enhances interpretability but also facilitates a deeper understanding of the underlying data patterns and relationships. Moreover, it can guide feature selection and inform strategic decisions, ensuring a more robust and transparent modeling process. In our pursuit of enhancing both model performance and fairness, we applied the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and further  
Figure 1: Feature importance of Logistic Regression for MBA Dataset. balance the dataset. However, these efforts did not result in notable improvements across key performance metrics, including accuracy, precision, recall, F1-score, and Matthews correlation coefficient (MCC). Similarly, there was no discernible progress in fairness metrics, such as demographic parity and equalized odds. To build on this, we explored ensemble techniques designed to harness the strengths of the top-performing models with respect to both accuracy and fairness. Specifically, we implemented hard voting and soft voting ensemble methods, which combine predictions from multiple classifiers to create a potentially more robust aggregated model. Unfortunately, these ensemble strategies also failed to deliver improvements in either performance or fairness metrics. This outcome suggests that the individual models may already be close to their optimal performance given the constraints imposed by the dataset. These findings highlight the inherent challenges posed by the dataset's characteristics, including its class imbalance and demographic disparities. Traditional data balancing techniques, such as SMOTE, and model aggregation strategies, like ensemble methods, appear insufficient to overcome these limitations.