## 4 Experiments and Analysis
### Classification Prediction Experiments
Thirdly the gradient boosting model demonstrated relatively strong performance across all categories as it achieved the highest accuracy in classifying samples as \"Pass\", \"Fair\", and \"Good\". This effectiveness could be attributed to gradient boosting being a comprehensive learning approach that effectively captures complex patterns within the dataset. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Prediction** & \multicolumn{1}{c}{**Evaluation Metrics**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Recall**} & \multicolumn{1}{c}{**F1-Score**} & \multicolumn{1}{c}{**Support**} \\ \hline \multirow{8}{*}{Random Forest} & Fail & 0.00 & 0.00 & 0.00 & 4 \\  & Pass & 0.54 & 0.58 & 0.56 & 24 \\  & Fair & 0.38 & 0.48 & 0.43 & 31 \\  & Good & 0.50 & 0.33 & 0.40 & 21 \\  & Excellent & 0.44 & 0.50 & 0.47 & 8 \\  & Accuracy & – & – & 0.45 & 88 \\  & Macro Avg & 0.37 & 0.38 & 0.37 & 88 \\  & Weighted Avg & 0.44 & 0.45 & 0.44 & 88 \\ \hline \multirow{8}{*}{SVM} & Fail & 0.00 & 0.00 & 0.00 & 4 \\  & Pass & 0.63 & 0.50 & 0.56 & 24 \\  & Fair & 0.43 & 0.65 & 0.52 & 31 \\  & Good & 0.50 & 0.33 & 0.40 & 21 \\  & Excellent & 0.44 & 0.50 & 0.47 & 8 \\  & Accuracy & – & – & 0.49 & 88 \\  & Macro Avg & 0.40 & 0.40 & 0.39 & 88 \\  & Weighted Avg & 0.49 & 0.49 & 0.47 & 88 \\ \hline \multirow{8}{*}{Gradient Boosting} & Fail & 0.00 & 0.00 & 0.00 & 4 \\  & Pass & 0.58 & 0.62 & 0.60 & 24 \\  & Fair & 0.50 & 0.52 & 0.51 & 31 \\  & Good & 0.50 & 0.48 & 0.49 & 21 \\  & Excellent & 0.40 & 0.50 & 0.44 & 8 \\  & Accuracy & – & – & 0.51 & 88 \\  & Macro Avg & 0.40 & 0.42 & 0.41 & 88 \\  & Weighted Avg & 0.49 & 0.51 & 0.50 & 88 \\ \hline \multirow{8}{*}{K-Neighbors} & Fail & 0.00 & 0.00 & 0.00 & 4 \\  & Pass & 0.53 & 0.42 & 0.47 & 24 \\  & Fair & 0.36 & 0.52 & 0.43 & 31 \\  & Good & 0.42 & 0.24 & 0.30 & 21 \\  & Excellent & 0.31 & 0.50 & 0.38 & 8 \\  & Accuracy & – & – & 0.40 & 88 \\  & Macro Avg & 0.32 & 0.33 & 0.32 & 88 \\  & Weighted Avg & 0.40 & 0.40 & 0.38 & 88 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Prediction accuracy comparison of category 5. Fourthly, the K-neighbors model displayed good performance in correctly categorizing samples as \"Pass\" and \"Fair\", but struggled in classifying samples as \"Good\", \"Very Good\", and \"Excellent\". This limitation can be attributed to the K-neighbors model's sensitivity to the dataset's distribution and feature selection. Fifthly, the AdaBoost model showcased strong performance in correctly classifying samples as \"Pass\", \"Fair\", and \"Good\", but performed less accurately in classifying samples as \"Very Good\" and \"Excellent\". This could be due to the AdaBoost model's susceptibility to overfitting and limited capability to handle complex datasets. In summary, based on the overall accuracy, macro average, and weighted average evaluation indicators, the K-neighbors model exhibited the highest performance, followed by the SVM model. On the other hand, the random forest, gradient boosting, and AdaBoost models displayed comparatively weaker performance. It is important to note that the performance of each model varied across different categories, with random forest performing poorly in the \"Fail\" category and SVM performing better in the \"Pass\" category. An additional visual comparison should be provided, presenting the model accuracy for two, three, and five classification categories using appropriate graphical representations. In addition, considering additional evaluation metrics, such as precision, recall, and F1-score, would provide a more comprehensive assessment of each model's performance and its ability to correctly identify positive and negative instances. According to the above data, this paper presents a visual comparison of the model accuracy of category 2, category 3, and category 5 prediction of the five machine learning algorithms, as shown in Figure 4: