Income Evaluation

This project focuses on predicting income levels (either less/equal to $50K or greater than $50K) using a dataset with various demographic and employment features. Data preprocessing involved renaming columns, converting the target variable to binary, and handling missing values by removing affected rows. Categorical features such as 'workclass', 'education', 'marital_status', 'occupation', and 'sex' were numerically encoded for model compatibility. A Logistic Regression model was trained and evaluated, achieving an accuracy of approximately 78.5% on the test set.