Application of Decision Tree Algorithm with Hyperparameter Tuning for Income Level Classification Based on Demographic and Occupational Factors
DOI:
https://doi.org/10.22303/it.14.1.2026.86-100Keywords:
Decision Tree, Hyperparameter Tuning, GridSearchCV, Income Classification, Data MiningAbstract
Income level classification is an important problem in socio-economic data analysis because it can support decision-making in social policy, marketing, and human resource management. This research aims to apply the Decision Tree algorithm to classify an individual's income level into two categories, less than or equal to 50,000 US dollars per year and more than 50,000 US dollars per year, based on demographic and occupational attributes such as age, education level, marital status, occupation, and hours worked per week. The research methodology consists of data collection, data cleaning, categorical attribute encoding, train-test splitting, Decision Tree model construction, and hyperparameter optimization using GridSearchCV with 5-fold cross-validation. The results show that the model with initial parameters achieved an accuracy of 78.92%, while after hyperparameter tuning the accuracy increased to 80.54%, with the optimal parameters being criterion=entropy, max_depth=8, and min_samples_leaf=5. Precision also increased from 56.76% to 64.29%. Feature importance analysis revealed that relationship, age, and education-num are the most influential attributes in predicting income level. This study demonstrates that hyperparameter tuning can significantly improve the performance of a Decision Tree model in an income-level classification task.
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