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

Description

This dataset contains a real Adult Income dataset and an author-generated synthetic income dataset prepared for comparative statistical and machine learning analysis. The real dataset is based on the Adult dataset from the UCI Machine Learning Repository, while the synthetic dataset was developed as a Bangladesh-oriented tabular income dataset using demographic, educational, employment, working-hour, experience, and income-related variables. The datasets are provided to support the comparison of real and synthetic tabular income data in terms of their structure, distributions, statistical relationships, and usefulness for machine learning experiments. The accompanying analysis includes data inspection, preprocessing, descriptive statistics, correlation analysis, statistical testing, classification experiments, class-imbalance handling, and model evaluation. 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.

Steps to reproduce

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors

Institutions

• East West University

  Dhaka Division, Dhaka

Categories

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