In this study, a classification problem is discussed to predict whether individuals' annual income levels are above $50,000. The UCI Adult Income Dataset, which is derived from the 1994 US Census (Census) data and is widely used in the field of machine learning, was preferred as the data source. This dataset contains 48,842 samples and 14 independent variables; It covers categorical and numerical characteristics such as age, educational status, marital status, profession. In addition, some categorical variables in the dataset have missing values and the classes of the target variable are unbalanced. Individuals with incomes below 50K account for 76%, while those above 24%. Therefore, in order to interpret model performance more accurately, metrics such as the ROC curve and AUC (Area Under Curve) were taken into account as well as the accuracy rate. In the study, after the data analysis and preprocessing process was completed, four different classification algorithms were applied: Logistic Regression, Support Vector Machines (SVM), Random Forest and Naive Bayes. The Logistic Regression model achieved an accuracy rate of 83.2%, while the SVM algorithm showed a moderate performance with an accuracy of 80.3%. While the Naive Bayes algorithm was moderately successful with an accuracy of 78.7%, the highest accuracy rate was obtained in the Random Forest algorithm with 85.1%. These results revealed that despite the unbalanced class distribution, the Random Forest algorithm is an effective method in such classification problems thanks to its high accuracy.

Keywords - Machine Learning, Income Estimation, Income Classification, UCI Adult Dataset, Data Analysis, Logistic Regression, Support Vector Machine, Random Forest, Naive Bayes, Classification Algorithms, Model

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