## 4 Experiments and Analysis
### Classification Prediction Experiments
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Prediction** & **Evaluation Metrics** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline \multirow{4}{*}{Random Forest} & Fail & 0.00 & 0.00 & 0.00 & 4 \\  & Pass & 0.95 & 1.00 & 0.98 & 84 \\  & Accuracy & – & – & 0.95 & 88 \\  & Macro Avg & 0.48 & 0.50 & 0.49 & 88 \\  & Weighted Avg & 0.91 & 0.95 & 0.93 & 88 \\ \hline \multirow{4}{*}{SVM} & Fail & 0.00 & 0.00 & 0.00 & 4 \\  & Pass & 0.95 & 1.00 & 0.98 & 84 \\  & Accuracy & – & – & 0.95 & 88 \\  & Macro Avg & 0.48 & 0.50 & 0.49 & 88 \\  & Weighted Avg & 0.91 & 0.95 & 0.93 & 88 \\ \hline \multirow{4}{*}{K-Neighbors} & Fail & 0.00 & 0.00 & 0.00 & 4 \\  & Pass & 0.95 & 1.00 & 0.98 & 84 \\  & Pass & 0.95 & 1.00 & 0.98 & 84 \\  & Accuracy & – & – & 0.95 & 88 \\  & Macro Avg & 0.48 & 0.50 & 0.49 & 88 \\  & Weighted Avg & 0.91 & 0.95 & 0.93 & 88 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Prediction accuracy comparison of category 2. Overall, it seems that the random forest and SVM models perform slightly better than the others in terms of precision and recall for the \"Pass\" and \"Excellent\" categories. However, it is important to consider other factors such as computational efficiency, interpretability, and the specific requirements of the prediction task when selecting the most suitable model. Additionally, the accuracy score for all models ranges from 0.72 to 0.76, indicating that the overall accuracy of the models is relatively high. In summary, while all models demonstrate good performance in predicting the \"Pass\" and \"Excellent\" categories, there is room for improvement in predicting the \"Fail\" category. It may be worth considering further model optimization or exploring alternative algorithms to improve performance in all categories. Based on the above results of Table 9, the following analysis conclusions can be made:  
Firstly, the performance of the random forest model was found to be superior in correctly categorizing samples as \"Pass\" and \"Fair\", but less accurate in classifying samples as \"Good\" and \"Excellent\". This suggests that the model is more effective at distinguishing between normal and slightly inferior samples but struggles with distinguishing between excellent and outstanding samples. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Prediction Model** & **Evaluation Metrics** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline \multirow{6}{*}{Random Forest} & Fail & 0.00 & 0.00 & 0.00 & 4 \\  & Pass & 0.77 & 0.89 & 0.82 & 55 \\  & Excellent & 0.75 & 0.62 & 0.68 & 29 \\  & Accuracy & – & – & 0.76 & 88 \\  & Macro Avg & 0.51 & 0.50 & 0.50 & 88 \\  & Weighted Avg & 0.73 & 0.76 & 0.74 & 88 \\ \hline \multirow{6}{*}{SVM} & Fail & 0.00 & 0.00 & 0.00 & 4 \\  & Pass & 0.77 & 0.85 & 0.81 & 55 \\  & Excellent & 0.70 & 0.66 & 0.68 & 29 \\  & Accuracy & – & – & 0.75 & 88 \\  & Macro Avg & 0.49 & 0.50 & 0.50 & 88 \\  & Weighted Avg & 0.71 & 0.75 & 0.73 & 88 \\ \hline \multirow{6}{*}{Gradient Boosting} & Fail & 0.00 & 0.00 & 0.00 & 4 \\  & Pass & 0.75 & 0.87 & 0.81 & 55 \\  & Excellent & 0.71 & 0.59 & 0.64 & 29 \\  & Accuracy & – & – & 0.74 & 88 \\  & Macro Avg & 0.49 & 0.49 & 0.48 & 88 \\  & Weighted Avg & 0.70 & 0.74 & 0.72 & 88 \\ \hline \multirow{6}{*}{K-Neighbors} & Fail & 0.00 & 0.00 & 0.00 & 4 \\  & Pass & 0.76 & 0.82 & 0.79 & 55 \\  & Excellent & 0.66 & 0.66 & 0.66 & 29 \\  & Accuracy & – & – & 0.73 & 88 \\  & Macro Avg & 0.47 & 0.49 & 0.48 & 88 \\  & Weighted Avg & 0.69 & 0.73 & 0.71 & 88 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Prediction accuracy comparison of category 3. Secondly, the SVM model exhibited higher accuracy in correctly classifying samples as \"Pass\", but lower accuracy in classifying samples as \"Fair\" and \"Excellent\". This could potentially be attributed to the SVM model encountering challenges when dealing with nonlinear separable data.