## 5 Discussion  
Ports are important hubs in logistics and supply chain systems, where the majority of the available data is still not being fully exploited. Machine learning techniques are flexible tools for utilizing and unlocking the value of the data. The port sector is significantly behind other forms of transportation in this change, which is surprising given the rapidly expanding usage of machine learning as a tool for data-driven prediction.  
In this paper, we proposed a two-stage model consisting of a feature selection method involving neighborhood component analysis, followed by a classification task using l1 state-of-the-art machine learning algorithms. In step I of the proposed approach, we obtained  
Figure 5: The ROC Curves show AUC values. Order of appearance of sub-figures (from left to right): a = Decision Tree (Model 6.1), b = Linear Discriminant Analysis (Model 6.4), c = Quadratic Discriminant Analysis (Model 6.5), d = Linear Support Vector Machine (Model 6.6), e = Quadratic Support Vector Machine (Model 6.7), f = Cubic Support Vector Machine (Model 6.8), g = Gaussian Support Vector Machine (Model 6.10), h = K-Nearest Neighbor (Model 6.15), i = Boosted Tree Ensemble (Model 6.18), j = Bagged Tree Ensemble (Model 6.19), and k = Subspace Discriminant Ensemble Learner (Model 6.20).  
seven unique features, which are the essential parameters for the identification of the vessel size. The selected features are Import box, 4000% Loading, Loading single, 20_Box, 40_Box, TW, and LD. Here, the container capacity plays a vital role in distinguishing the size of the vessel. Out of these seven features, we also made an effort to tune the algorithm with different permutations. However, we found that the set of these seven parameters can achieve high classification accuracy. It is worth mentioning that we achieved the highest classification accuracy of 97.6% and AUC = 1 with the support vector machine classifier with a linear kernel. Alongside this, we also achieved promising classification accuracies with other algorithms (as shown in Table 3).  
Figures 3 and 4 show the confusion matrixes and the ROC curves for all the state-of-the-art machine learning algorithms used in this study. The promising results, as shown in the figures, prove the efficacy of our proposed method. Most of the ML classifiers showed high classification accuracy for these seven distinct parameters. The AUC values of 1 or close to one signify the classifiers are well-trained and well-chosen for this dataset.  
The finally selected 7 out of 39 parameters are essential data derived through the quantitative work of berthed ships, and these data are the basic items for calculating the operational performance and throughput of the port terminal.  
The seven parameters selected through this study are ultimately used as the basic data for the calculation of throughput performed at the container terminal. QC allocation prediction for each ship was performed through the seven parameters derived in this study. In the future, container terminals are changing into unmanned and automated ports. Therefore, it will be possible to use basic data to calculate the container throughput in the unmanned automated port and the number of ships that can be handled in the unmanned automated port through the seven parameters presented in this study. This study, which incorporates a machine learning-based study, paves the way for future research in port logistics.