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rodrick-mpofu/README.md

👋 Hi there!

I'm Rodrick Mpofu, a recent Data Science graduate from St. Lawrence University with a passion for turning data into actionable insights. I specialize in machine learning, AI applications, and data-driven problem solving, and I enjoy exploring innovative ways to build models that make an impact.

I’ve worked on projects ranging from predictive modeling and explainable AI in computer vision to building data pipelines and analyzing large datasets. I’m always looking to learn new tools, techniques, and frameworks to improve my workflow and contribute to meaningful projects.

Outside of tech, I’m a big soccer (football) fan, love dancing, and enjoy exploring music, especially DJing and creating playlists. ⚽💃🎶

Feel free to check out my projects and notebooks below — I love sharing work that demonstrates both creativity and technical skill!

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  1. fastapi-movie-watchlist fastapi-movie-watchlist Public

    RESTful Movie Watchlist API built with FastAPI and Python. Manage user watchlists with async endpoints, Pydantic validation, and in-memory storage.

    Python 1

  2. model-explainability model-explainability Public

    A Shiny-based web application that enables users to upload images and analyze how deep learning models make predictions.

    Python 1

  3. puter puter Public

    Forked from HeyPuter/puter

    🌐 The Internet OS! Free, Open-Source, and Self-Hostable.

    JavaScript

  4. sim sim Public

    Forked from simstudioai/sim

    Open-source AI Agent workflow builder.

    TypeScript

  5. kaggle-playground-series kaggle-playground-series Public

    A collection of my solutions to Kaggle Playground Series competitions, showcasing end-to-end machine learning workflows including data cleaning, feature engineering, model building, and evaluation.…

    Jupyter Notebook

  6. counterdistill counterdistill Public

    Explainable ML pipeline that clusters feasible counterfactuals and distills them into global interpretable rules using DiCE, SHAP, Polars, DuckDB, and scikit-learn.

    Python