## 4 Experimental Results
### Data Processing and Classification  
After evaluating the raw data, preprocessing steps were applied to the dataset. Samples with missing values and outliers in the features related to payment habits were removed. Duplicate records were handled in two ways: (1) all samples with different values were deleted, (2) samples with the same values were retained only once. Given that the data source has a relational structure, the dataset was also evaluated in terms of data structure and quality. Records that did not correspond to any institution, product, and invoice type were deleted. After the dataset was cleaned of defective data, the normalization and feature selection processes were performed. The class label distribution before and after the preprocessing step is shown in Figure 2. After preprocessing, the dataset consists of 29,817 samples.  
In this study, the ANOVA F-Test, chi-square, and mutual information were used as feature selection methods; SMOTE was also chosen as the oversampling method. After feature selection, the dataset was trained by balancing with SMOTE and then tests were performed. All the classification algorithms in Table 6 were also used in this step. The best results were obtained with the EXT classification method. The most successful results based on feature selection method are presented in Table 7.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Class.**} & \multicolumn{6}{c}{**Evaluation Metrics (\%)**} \\ \cline{2-7}  & **ACC** & **PRE** & **REC** & **F1** & **F2** & **AUC** \\ \hline LR & 29.12 & 25.55 & 29.12 & 26.56 & 27.91 & 72.73 \\ \hline SVM & 30.19 & 26.46 & 30.19 & 26.40 & 28.25 & 75.68 \\ \hline DT & 42.72 & 43.02 & 42.85 & 42.67 & 42.74 & 68.12 \\ \hline RF & 47.65 & 48.38 & 47.52 & 47.06 & 47.18 & 85.93 \\ \hline EXT & 47.07 & 47.31 & 47.11 & 46.60 & 46.87 & 85.46 \\ \hline NB & 20.60 & 14.70 & 20.60 & 13.57 & 16.19 & 69.38 \\ \hline MLP & 44.37 & 45.71 & 44.48 & 43.98 & 44.38 & 85.57 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification Results with Raw Data.  
Table 7 shows that all three feature selection methods yielded the best performance by selecting 10 features. ANOVA method; PRODUCT_CODE and PAYMENT_AMOUNT_AVG, Chi-square method; TOTAL_PAYMENT_AMOUNT and PAYMENT_AMOUNT_AVG, mutual information method; GENDER and INVOICE_TYPE features were not selected. When the preferences of the feature selection methods are considered, the CASH_PERCENTAGE, LATEST_PAYMENT_RATE, AVG_DAYS_PAID_LATE, AVG_DAYS_PAID_EARLY, and NUMBER_OF_INVOICE_TYPES features indicating payment habits were used in each feature selection method.  
Figure 2: Label Distribution Before and After Preprocessing.  
method and were found to be important parameters. The AGE feature indicating the age of the customer was also used in all methods. The best overall performance was achieved using the EXT classification algorithm. Among the feature selection methods, the ANOVA F-test yielded the most successful results across all evaluation metrics. Specifically, this configuration achieved an accuracy of 80.49% \(\pm\) 1.12 and an AUC of 97.04% \(\pm\) 0.85. The precision, recall, F1, and F2 scores were also higher than those of the other models. In addition, the relatively low standard deviations observed indicate high stability and consistency across cross-validation folds.