## 4 Results and Discussion  
In this section, the dataset and developed models such as the Long Short-Term Memory (LSTM) and One Dimensional Convolutional Neural Network (1D-CNN) were subjected to repeated K fold cross-validation. Python 3.6, numpy, sklearn, keras, imblearn, pandas, matplotlib, seaborn, and plotly are the python packages used in this work. The hyperopt library in Python is used to perform Bayesian hyperparameter optimization. The supply chain dataset's missing values were shown using a heatmap. The heatmap in Figure 3 shows two colors: red for missing data and green for remaining values with no Nan values. Shipment Mode, Dosage, and Line-Item Insurance contain missing values. Correlation heatmaps are heatmaps that display the degree of correlations between numerical variables. The links between variables and their strength are visualized using correlation graphs. Every numerical variable in a correlation plot is typically represented by a column. The rows reflect the connections between each pair of variables. The values in the cells represent the strength of the correlation; positive values represent a positive relationship, while negative values represent a negative association. The strength of potential relationships between variables may be evaluated using heatmaps of correlation. Additionally, linear, and nonlinear correlations, as well as outliers, may be found using correlation plots. It is easy to quickly spot relationships between variables because of the color coding of the cells. Correlation heatmaps may be used to discover both linear and nonlinear relationships between data. Line-Item Quantity is substantially connected with Line-Item Insurance, as seen in Figure 4, with a correlation value of 0.8. Meanwhile, the Pack Price has a 0.6 correlation coefficient with Unit Price, although Unit Price is unaffected by Unit of Measure or ID. The data peaks are displayed using a violin plot, which is a mix between a box plot and a kernel density plot. It serves as a representation of the distribution of numerical data. Violin plots display summary statistics as well as the density of each variable, as opposed to box plots, which can only offer summary statistics. The median can be seen as a white dot in violin plots. Conversely, the narrow gray line represents the remaining portion of the distribution, while the wide gray bar in the middle displays the interquartile range. On either side of the gray line, a kernel density estimation is displayed to demonstrate how the data are distributed. The violin plot is divided between broader and skinnier parts, with wider areas representing a greater likelihood that members of the population would adopt the given value and skinnier areas representing a lower probability. As seen in Figure 5, the median Pack Price for Pediatric and Adult is about 1 but with a greater likelihood, whereas the median Pack Price for ACT is around 45 but with a lower chance. Furthermore, with a decreased chance, the median Pack Price for Malaria is about 30. Figure 3: Detecting Missing values in Supply Chain Shipment Dataset. Figure 4: Correlation Coefficient of Supply Shipment Pricing Dataset. Figure 5: Violin Plot of Pack Price vs Sub Classification. The performance of the deep learning models used in this study was enhanced with the aid of repeated k-fold cross-validation. This merely involves repeating the cross-validation process and reporting the average outcome across all folds from all runs. The supply chain shipping data are separated into five folds and repeated twice in this approach, as shown in Tables 1-6. When training our models, we must consider loss and accuracy. Loss is a metric that represents the sum of our model's errors. It determines how well or poorly our model is performing. Furthermore, accuracy assesses how well our model predicts by comparing model predictions to true values in percentage terms. Table 1 displays the Loss and Accuracy of Long Short-Term Memory (LSTM).