## 6 Conclusions  
The study aimed to identify the important features using a dataset when predicting student performance and identifying the most suitable qualifications. We identified important features and used these in model training. Moreover, the specified features can be utilised to determine whether a student is better suited for mainstream or extended qualifications. This study adds to the literature on improving academic advising in engineering education by using a machine-learning approach. This study used a real-world dataset from a public university in South Africa to identify features that significantly influence the task of identifying the most suitable qualifications from a dataset. The study also assessed and compared how well various classification techniques performed using these features. This study employed a comprehensive methodology encompassing data pre-processing, feature engineering, imbalanced data handling, and model training. The study showed that mathematics, physical sciences, and APS are important features when predicting student performance and qualification enrolment. These features were used in models to predict overall student performance, first-year student performance, qualification enrollment for all students, qualification enrollment for first-year students, and predicting the qualification flag for first-year students successfully. The KNN model exhibited exceptional performance in predicting overall student performance, while decision trees, LGBM, and random forest models also excelled. When predicting first-year student performance, all models demonstrated equally high accuracy. When predicting qualification enrollment, models yielded consistently strong performance, with decision trees and random forests leading the pack. These findings underscore the value of features like APS, mathematics, and physical sciences in predicting first-year student performance. This study represents a positive step toward harnessing student data to improve academic advising and promote student success. It highlights the potential of data-driven interventions to address student attrition in engineering education. As education continues to evolve, incorporating predictive modelling into academic advising can contribute to more personalised and effective student support. Given the strong influence of features like mathematics, physical sciences, and APS on predicting student success, it is possible to use these features as important inputs in recommender systems that incorporate student performance data from high school. These recommender systems can be used by faculty-student advisors to improve decision-making in student advising. By combining these data-driven advising systems, educational institutions may better serve students on an ongoing basis, help them succeed academically, and increase their overall success rates. The application of recommender systems by student-faculty advisors has the potential to revolutionise how students navigate their academic and professional journeys. This study shows that using a real-world dataset makes accurate prediction of student performance and qualifications possible. These models can be incorporated into recommender systems to better support students in their academic and professional careers by arming faculty-student advisors with making data-driven recommendations. This can save student-faculty advisors time and effort since they will not have to manually access enormous amounts of data. This aligns with a study that showed that recommender systems enable advisors to evaluate a broader range of scenarios efficiently, facilitating better-informed and cost-effective decisions, particularly in challenging situations [69]. There are a few limitations to this study. The study's use of the engineering student cohort from 2016 and 2017 has several limitations due to the limited range of the data that can be analysed.