# Predictive Analysis for Optimizing Port Operations
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RMSE** & \(R^{2}\) & **RMSLE** & **MAPE** \\ \hline
**Random Forest** & 0.8102 & 17.8800 & **4.2179** & 0.6160 & 0.4997 & 0.5579 \\ ResNet & 1.2444 & 17.8963 & 4.2304 & 0.6165 & 0.5610 & 0.6188 \\ Extreme Gradient Boosting & 1.2285 & 18.1063 & 4.2470 & 0.6112 & 0.6103 & 0.7758 \\ Extra Trees & 0.7304 & 19.8816 & 4.4488 & 0.5729 & 0.4981 & 0.5186 \\ Light Gradient Boosting Machine & 1.3226 & 20.9450 & 4.5700 & 0.5502 & 0.6241 & 0.8042 \\ Neural Network & 1.4564 & 24.6622 & 4.9661 & 0.4715 & 0.7127 & 0.8734 \\ Decision Tree & 0.7571 & 26.6984 & 5.1538 & 0.4255 & 0.5464 & 0.5569 \\ Gradient Boosting & 1.7824 & 29.9394 & 5.4656 & 0.3574 & 0.7811 & 0.9428 \\ K Nearest Neighbors & 1.3783 & 35.7453 & 5.9735 & 0.2310 & 0.6818 & 0.7387 \\ Bayesian Ridge & 2.3272 & 42.8552 & 6.5377 & 0.0815 & 0.9559 & 1.1375 \\ Ridge Regression & 2.3273 & 42.8554 & 6.5378 & 0.0815 & 0.9564 & 1.1423 \\ Linear Regression & 2.3271 & 42.8556 & 6.5378 & 0.0815 & 0.9564 & 1.1425 \\ Elastic Net & 2.0899 & 44.3601 & 6.6513 & 0.0494 & 0.8636 & 0.9716 \\ Lasso Regression & 2.1192 & 44.7360 & 6.6794 & 0.0414 & 0.8685 & 0.9946 \\ Huber Regression & 1.2250 & 48.0613 & 6.9232 & \(-\)0.0299 & 0.7032 & 0.9955 \\ AdaBoost & 3.2974 & 54.0987 & 7.3534 & \(-\)0.1708 & 1.1535 & 1.6455 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparing 10-fold CV results of different methods with multiple metrics (sorted using RMSE) for Delay Time prediction at port. Figure 7: R-squared plot on test data for Delay Time prediction. Figure 8: Random Forest feature importance for Delay Time prediction. Figure 10: SHAP decision plot for Delay Time prediction of a sample observation. Figure 9: SHAP feature analysis plot for Delay Time prediction. In the classification task, the performance of various Machine Learning methods for (Multi-Class) classification of Total Time is depicted in Table 3. A consistent trend is observed where tree-based methods outperform statistical approaches as well as deep learning methods again, potentially due to the complex and tabular nature of the data along with the high number of categorical features. Specifically, Extreme Gradient Boosting (XGBoost) demonstrates the best performance, achieving the highest Accuracy (0.8424) and superior values across other metrics compared to alternative methods. This is 10% higher than the result in [5]. The test set results further confirm XGBoost's efficacy with an Accuracy of 0.8415 and other metrics exhibiting similarly favorable performance, derived from the confusion matrix seen in Figure 12. Also, it is easier to detect the outer classes, Class 0 (Low) and Class 3 (Very High), compared to the other two for this case study. Additionally, Figure 13 showcases the key features influencing Total Time classification. Notably, the Geographical Region (North), Port Complex (Manaus), and Type of Mooring Navigation (inside) emerge as the most influential features, followed by factors such as City, Mooring port, Inland Transportation route, Type of Mooring Navigation (Long Haul), Berth, etc. The significance of the geographical features is related to the huge coastline of Brazil and the complex dynamics of port operations.