## 4 Results and Discussion
The Loss is within the range of 1.3907 and 1.4682, which means that the model does not perform well. Also, the accuracy in Table 1 is within the range of 50 and 55 which shows that the accuracy is low. Since the loss is low and accuracy is low, it means the LSTM model is not performing well. Table 2 displays the Loss and Accuracy of the One-Dimensional Convolutional Neural Network (1D-CNN). The Loss is within the range of 1.3 and 1.4 while the accuracy is within the range of 52 and 55. Both the loss and accuracy were very low. It means that 1D-CNN is not performing well. \begin{table}
\begin{tabular}{c c c} \hline
**Iteration** & **Loss (\%)** & **Accuracy (\%)** \\ \hline FOLD 1 & \(1.4142\times 10^{-7}\) & 54.0441 \\ FOLD 2 & \(1.3936\times 10^{-7}\) & 55.2696 \\ FOLD 3 & \(1.4682\times 10^{-7}\) & 51.9607 \\ FOLD 4 & \(1.4419\times 10^{-7}\) & 52.5735 \\ FOLD 5 & \(1.3907\times 10^{-7}\) & 52.9411 \\ FOLD 6 & \(1.4053\times 10^{-7}\) & 53.7990 \\ FOLD 7 & \(1.4725\times 10^{-7}\) & 50.9803 \\ FOLD 8 & \(1.4010\times 10^{-7}\) & 55.1470 \\ FOLD 9 & \(1.4144\times 10^{-7}\) & 54.3558 \\ FOLD 10 & \(1.4071\times 10^{-7}\) & 53.4969 \\ \hline \end{tabular}
\end{table} 
Table 1: Loss and Accuracy of Long Short-Term Memory (LSTM). \begin{table}
\begin{tabular}{c c c} \hline
**Iteration** & **Loss (\%)** & **Accuracy (\%)** \\ \hline FOLD 1 & \(1.4740\times 10^{-7}\) & 52.4509 \\ FOLD 2 & \(1.4287\times 10^{-7}\) & 53.4313 \\ FOLD 3 & \(1.4243\times 10^{-7}\) & 53.4313 \\ FOLD 4 & \(1.4302\times 10^{-7}\) & 52.4509 \\ FOLD 5 & \(1.4287\times 10^{-7}\) & 53.3088 \\ FOLD 6 & \(1.4316\times 10^{-7}\) & 53.0637 \\ FOLD 7 & \(1.4243\times 10^{-7}\) & 54.0441 \\ FOLD 8 & \(1.3294\times 10^{-7}\) & 55.5147 \\ FOLD 9 & \(1.4539\times 10^{-7}\) & 52.3926 \\ FOLD 10 & \(1.3837\times 10^{-7}\) & 54.4785 \\ \hline \end{tabular}
\end{table} 
Table 2: Loss and Accuracy of One Dimensional Convolutional Neural Network (1D-CNN). \begin{table}
\begin{tabular}{c c c} \hline
**Iteration** & **Loss (\%)** & **Accuracy (\%)** \\ \hline FOLD 1 & \(1.4740\times 10^{-7}\) & 52.4509 \\ FOLD 2 & \(1.4287\times 10^{-7}\) & 53.4313 \\ FOLD 3 & \(1.4243\times 10^{-7}\) & 53.4313 \\ FOLD 4 & \(1.4302\times 10^{-7}\) & 52.4509 \\ FOLD 5 & \(1.4287\times 10^{-7}\) & 53.3088 \\ FOLD 6 & \(1.4316\times 10^{-7}\) & 53.0637 \\ FOLD 7 & \(1.4243\times 10^{-7}\) & 54.0441 \\ FOLD 8 & \(1.3294\times 10^{-7}\) & 55.5147 \\ FOLD 9 & \(1.4539\times 10^{-7}\) & 52.3926 \\ FOLD 10 & \(1.3837\times 10^{-7}\) & 54.4785 \\ \hline \end{tabular}
\end{table} 
Table 3: Loss and Accuracy of LSTM+AllKNN. By keeping all the data from the minority class and lowering the size of the majority class, under-sampling is a method for balancing disparate datasets. As demonstrated in Figure 1, there were uneven classes in target variables such as shipment mode. The dataset was undersampled using AllkNN. The Loss and Accuracy of LSTM and 1D-CNN with AllKNN are shown in Tables 3 and 4. The accuracy of LSTM has increased from 50% to 63%, indicating that the model outperforms the prior model. To fine-tune the deep learning methods in Tables 3 and 4, Bayesian Optimization (BO) using a tree-structured parzen estimator was used. Bayesian Optimization is a method for finding the lowest or maximum of an objective function that uses the Bayes Theorem to guide the search. In many real-world analytics applications, optimizing a function is critical. By optimization, we imply determining the objective function maximum or minimum with a certain set of parameter combinations. Tables 5 and 6 show the outcomes of hyperparameter adjustment for LSTM+AllkNN and 1D-CNN+AllkNN. Tables 5 and 6 show a slight improvement in accuracy, indicating that the accuracy is performing well. Table 7 shows all of the parameters used in LSTM+AllkNN and 1D-CNN+AllkNN before and after Bayesian Optimization using the