Real and Synthetic Income Datasets for Comparative Statistical and Machine Learning Analysis

The machine learning analysis includes Logistic Regression, Decision Tree, Random Forest, and XGBoost, with additional experiments using class weighting and Synthetic Minority Over-sampling Technique (SMOTE) where applicable. Evaluation measures include accuracy, precision, recall, F1-score, ROC-AUC, and Balanced Error Rate (BER). The synthetic dataset is intended as a controlled data resource for methodological comparison and experimentation.

It should not be interpreted as nationally representative or as observed income data from the Bangladeshi population. In particular, the relationships within the synthetic data may reflect the assumptions and construction process used to generate the data rather than naturally occurring socioeconomic relationships. The repository is intended to support transparency, reproducibility, and future reuse of the datasets and associated analysis materials.

The real and synthetic datasets should be considered separately because their variables, target representations, and data-generation processes differ.