## 6 Discussion  
Six of the well-known machine learning algorithms were compared using a dataset about the activity of 261 third-year students in the university course. The dataset was pre-processed in a standard way. The models were trained and tested on the dataset from three academic years. The findings obtained are based on a balanced dataset. Measures of success have been recorded in both categories, completers and non-completers. Both biases were avoided in terms of an unbalanced dataset as well as based on averaging. Subsequently, the models were evaluated unseen data from the fourth academic year. The classifiers were evaluated using several performance metrics and using [PERSON]'s test, which compared their ability to classify the unseen cases in the same classes. The best dropout prediction based on the comparison of the precision, recall, accuracy and F1 scores was obtained by random forest (RF) classifier in general. The results obtained by the LR, SVM, and DT algorithms were the second-best with slight differences compared to RF. On the other hand, the Naive Bayes and the Neural Network model had the worst results overall and are not considered accurate classification models for this limited dataset. This finding confirms that the performance of the supervised machine learning models closely depends on enough records. The evaluation data was slightly different from the training data because the course has received minor changes compared to the previous runs. The NN's accuracy was mostly affected by these changes as the dataset consists of relatively few records. Standardization or normalization of the dataset was considered to improve the NN model. Finally, standardization of attributes was chosen as it is recommended and useful for models that depend on attribute distribution. The prediction accuracy varied between 77 and 93%, indicating that regardless of the model used, the chosen features in this study proved to be successful in predicting student's success or dropout even though the limitation of the dataset and number of features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **LR** & **DT** & **RF** & **NB** & **SVM** \\ \hline LR & - & \(p=1.000\) & \(p=0.500\) & \(p=0.001\) & \(p=1.000\) \\ DT & - & - & \(p=0.250\) & \(p=0.000\) & \(p=1.000\) \\ RF & - & - & - & \(p=0.007\) & \(p=0.250\) \\ NB & - & - & - & - & \(p=0.000\) \\ SVM & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: [PERSON]’s test comparison of the classifiers. On the other hand, all algorithms achieved recall values higher than 0.77 and simultaneously a very good precision. However, classification algorithms could achieve very high accuracy by only predicting the majority class. For instance, if any of the used algorithms predict all students as \"completers \({}^{\prime}1^{\prime\prime}\), the accuracy of the dataset is expected to be 78.5% because 21.5% of students dropped out, caused by the statistical distribution of the data. The examined predictive models were capable of reaching high performance in each class (completers '1' and non-completers '0') considering the values of recall and precision, as shown in Table 3. The current accuracy of the classification algorithms' predictions at this stage should be trusted. To sum up, there is a convergence between the metrics of RF, LR, SVM, and DT models. In addition, the harmonic mean of precision and recall was represented by the F-score. In addition, the F-score represented the harmonic mean of precision and recall, which indicates the overall success rate of prediction models to predict students who failed. Considering the F-score values shown in Table 3, the RF, LR, NN, and SVM models were comparatively useful in predicting students' likelihood to drop out. The performance of proposed classification algorithms was measured for further analysis regarding the effectiveness of various proposed models. The area under the receiver operating characteristic curve (AUC-ROC) was considered to evaluate the impact of an imbalanced dataset.