## 3 Methodology
### Data Preparation
#### 3.3.3 Feature Selection
The results, presented in Table 4, revealed a statistically significant correlation between \"Material_Ref\", \"Carrier_ID\", \"SAP_Customer_number\", and the variable \"Destination_Country\". As a result, the three aforementioned variables were excluded from further analysis, while \"Destination_Country\" was retained for subsequent analysis. To evaluate the correlation between a continuous numeric variable and a binary target variable, the point-biserial correlation coefficient was computed. This coefficient represents Pearson's product-moment correlation specifically designed for scenarios where one variable is dichotomous (binary) while the other is continuous [36]. The point-biserial correlation coefficient, denoted as \(r_{pb}\), ranges between \(-1\) and \(1\) (one). The sign of the coefficient indicates the direction of the relationship between the variables. A positive value signifies a positive association, implying that higher values of the continuous variable tend to be associated with the presence of the binary variable. Conversely, a negative value suggests a negative association, where higher values of the continuous variable are more likely to be linked with the absence of the binary variable. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & \(\chi^{2}\) & \(p\)**-Value** \\ \hline Material\_Ref & 165,937.62 & 0.00 \\ Carrier\_ID & 54,922.74 & 0.00 \\ SAP\_Customer\_number & 251,841.51 & 0.00 \\ WOOD\_PALLET & 0.83 & 0.061 \\ LAYER\_SEPARATOR & 0.77 & 0.082 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Chi-square independence test summary between the remainder categorical variables and “Destination_Country”. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & \(\chi^{2}\) & \(p\)**-Value** \\ \hline Material\_Ref & 2603.59 & \(3.05\times 10^{-78}\) \\ Destination\_Country & 249.03 & \(1.92\times 10^{-46}\) \\ External\_Warehouse(Y/N) & 0.18 & 0.67 \\ Carrier\_ID & 767.33 & \(2.74\times 10^{-41}\) \\ SAP\_Customer\_number & 1679.51 & \(5.49\times 10^{-48}\) \\ WOOD\_PALLET & 274.67 & \(4.38\times 10^{-46}\) \\ LAYER\_SEPARATOR & 163.29 & \(6.52\times 10^{-32}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Chi-square independence test summary: independent variables vs. “Complaint (Y/N)”. The magnitude, i.e., the absolute value, of \(r_{pb}\) indicates the strength of the relationship, with values closer to 1 (one) signifying a stronger association, while values closer to 0 (zero) indicate a weaker relationship, according to the following:  
\[r_{pb}=\frac{\bar{Y_{1}}-\bar{Y_{0}}}{\bar{s_{y}}}\times\sqrt{\frac{N_{1}\times N _{0}}{N\times(N-1)}}, \tag{2}\]  
where \(\bar{Y_{0}}\) and \(\bar{Y_{1}}\) are the mean of the continuous observations coded 0 (zero) and 1 (one), respectively, \(N_{0}\) and \(N_{1}\) the number of observations coded 0 (zero) and 1 (one), respectively, \(N\) the total number of observations (\(N_{0}+N_{1}\)), and \(\bar{s_{y}}\) the standard deviation of all the continuous observations, given as follows:  
\[\bar{s_{y}}=\frac{\sum Y^{2}-\frac{(\sum Y)^{2}}{N}}{N-1}. \tag{3}\]  
Table 5 summarizes the point-biserial correlation coefficient values (\(r_{pb}\)) and their corresponding \(p\)-values, which assess the relationship between the independent continuous variables and the target variable. These values offer insights into the strength and significance of the associations. Additionally, Figure 3 displays a point-biserial scatter plot illustrating the relationship between the continuous variable \"Temperature_Delivery_Date\" and the target variable \"Complaint (Y/N)\", providing a visual representation of their relationship.