I will share a purely categorical dataset and need it turned into a clear, well-documented end-to-end classification workflow that I can study for academic purposes. Using Python with Pandas, NumPy, scikit-learn, and visualisations in Matplotlib or Seaborn, start with an exploratory review, handle all cleaning and preprocessing (encoding, missing values, feature selection), then build and compare suitable classification models. Sound evaluation—accuracy, precision, recall, F1 or any metric you judge relevant—must accompany the models, followed by a concise discussion of the results and why a particular approach performs best.
Please highlight your experience with similar projects when you respond; I value demonstrated know-how over long proposals.
Deliverables I expect: • A well-commented Jupyter notebook covering EDA, preprocessing, model training, and evaluation • The cleaned dataset (or the code that generates it) • A brief markdown or slide deck that walks through the methodology, findings, and recommended next steps
Clarity of explanation is just as important as model accuracy, as the primary goal is learning from your workflow.
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