Developed a machine learning classification pipeline to predict whether an individual earns more ...Developed a machine learning classification pipeline to predict whether an individual earns more ...
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Kevin Williams
• Sep 24
Developed a machine learning classification pipeline to predict whether an individual earns more than $50K using demographic and employment data. The project included data cleaning, categorical encoding, feature engineering, model training, hyperparameter tuning, and performance evaluation.
Compared Logistic Regression, an MLP Neural Network, and Random Forest, with the tuned Random Forest achieving the strongest performance at 86.18% accuracy and 0.9154 ROC-AUC.
The project demonstrates an end-to-end classification workflow, including preprocessing mixed numerical and categorical data, comparing multiple modeling approaches, tuning model performance, and evaluating results using accuracy, ROC-AUC, and confusion-matrix analysis.
Completed work
Data AnalysisData ScienceJupyterPythonMachine Learning Engineeringscikit-learn
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