## 4 Results and Discussion
### Cross-Validation of Results for Accuracy  
If the average of each subject varies very little, but the average grades of each student vary drastically, it could suggest that individual factors are playing a significant role in student performance, thus cross-validation was performed, shown in Figure 19, to observe which of the individual factors are most relevant to student dropout. For Figure 19, the blue color represents individual factors, orange represents cohort, green represents race \begin{table}
\begin{tabular}{c c c} \hline \hline
**COHORT** & **SD** & **Mean** \\ \hline
3 & 2.18 & B \\ \hline
6 & 2.19 & B+ \\ \hline
4 & 2.19 & B \\ \hline
11 & 2.41 & B \\ \hline
5 & 2.41 & C+ \\ \hline
10 & 2.42 & B \\ \hline
2 & 2.45 & B \\ \hline
8 & 2.74 & B\(-\) \\ \hline
1 & 2.78 & B\(-\) \\ \hline
7 & 2.80 & B \\ \hline
12 & 2.89 & B \\ \hline
9 & 2.94 & B \\ \hline
13 & 3.05 & B \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Standard deviation per cohort ranked in increasing order. and red represents nationality. Furthermore, the x-axis lists the scale of importance from 0.0 to 0.7. Figure 19 indicates that students' backgrounds, such as race and nationality, and their cohort environment have minimal impact on dropout rates. Regarding the lower explanatory power of nationality and race compared to academic performance is indeed expected, given the context of the study. Since the students are from a single university in a single country, the variation in these parameters would naturally be limited, reducing their impact on predictive power. In our proposed study, the key finding was that individual academic performance had a more significant impact on student attrition than demographic factors like nationality and race. This is supported by the analysis, which shows that academic performance metrics (e.g., GPA, exam scores) contributed 60% to the predictive power, while background factors, including nationality and race, contributed only 30%. The minimal variation in average grades across subjects underscores the significant role of individual factors like learning style, motivation, study habits, personal interests, and mental health in student performance. Therefore, it is expected that academic performance would have a higher explanatory power, given the homogeneous nature of the student population in terms of nationality and race. This finding aligns with the expectation that in a more diverse setting, such demographic factors might have a more pronounced effect. However, in this study's context, the limited diversity leads to these factors having less impact compared to academic performance. The primary factors influencing dropout rates are individual characteristics. This observation is consistent with previous findings showing little connection between school environment or background and student attrition. The minimal variation in average grades across subjects underscores the significant role of individual factors like learning style, motivation, study habits, personal interests, and mental health in student performance. However, due to data limitations, further conclusions cannot be drawn. To enhance student success, educators should focus on recognizing and addressing individual factors affecting performance. This may include providing personalized instruction, accommodating various learning styles, and supporting students' social and emotional needs. The results of our study indicate that socio-economic status, attendance rates, and GPA are significant predictors of student attrition. The random forest model used in our analysis achieved an accuracy of close to 100% as observed from our hyperparameter optimization experiments. We recognize the importance of incorporating detailed statistical analysis to support our conclusions and elucidate the significance of the results. These findings suggest that early interventions targeting these factors could reduce dropout rates.