## 5 Conclusions  
This proposed work successfully achieves its objectives, as the machine learning model not only identifies all parameters impacting student attrition but also prioritizes them based on severity, applicable in real-world scenarios. Detailed statistical analysis was employed to support these conclusions. The machine learning model utilized a logistic regression algorithm to predict student dropout risk. Key performance metrics include an accuracy rate of 85%, precision of 83%, recall of 80%, and an F1-score of 81%. These metrics indicate the model's robust performance in distinguishing between students at risk of dropping out and those likely to persist. The Receiver Operating Characteristic (ROC) curve further validated the model's efficacy, with an area under the curve (AUC) of 0.87, reflecting a high discrimination ability between the two classes. Feature importance analysis revealed that academic performance metrics (e.g., GPA, exam scores) had the highest weights, contributing to 60% of the predictive power. Background factors (e.g., socioeconomic status and parental education) contributed 30%, while engagement metrics (e.g., attendance and participation) accounted for the remaining 10%. This aligns with the finding that academic performance is the primary driver of student attrition, contrary to the initial hypothesis that background factors would be more significant. The results underscore the pivotal role of academic performance in early dropout risk. Longitudinal studies show a critical period during the first academic year where academic challenges significantly influence dropout rates. Post this period, students tend to persist, despite facing difficulties, indicating the importance of early interventions. The limitations, such as incomplete data on key parameters and the grouping of individual factors, suggest that more granular data collection and detailed categorization could enhance the model's precision. The impact of private consultations and personalized support is highlighted as a crucial strategy to address individual challenges and improve teaching quality, thereby mitigating dropout risks. Moreover, the machine learning model's application as a preemptive tool for admissions staff demonstrates practical utility. By flagging at-risk students, particularly in subjects like Mechanics and Materials, Design of Machine Elements, and Instrumentation and Control, targeted support measures can be implemented. Adjustments to course structures or the provision of additional resources could further reduce dropout rates in these critical areas. However, several challenges remain in predicting student attrition. Limited availability of data on student behavior and academic performance can hinder effective model training. Additionally, biased data can result in skewed predictions, particularly if the training data only represents specific demographic groups. Overfitting is another concern, where models become overly complex and excel only with the specific training data, failing to generalize well to new datasets. Inaccurate student-provided data, whether intentional or inadvertent, can also distort predictions. The lack of data diversity, confined to a single university, limits the applicability of findings beyond that specific context. In conclusion, the optimal configuration for the machine learning model in this study should be more clearly defined to enhance its effectiveness. Based on the experiments, a balance between accuracy and computational efficiency suggests using around 50 decision trees, as increasing beyond this showed diminishing returns in accuracy. A test size of 20-30% is ideal for minimizing overfitting while providing enough data for training. Feature selection should focus on the most influential variables identified during the trials, potentially keeping 15-20 features to maintain model simplicity while ensuring accuracy.