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Focusing
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Focusing
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Achievements

Achievement: Pull Sharkx2Achievement: YOLOAchievement: Quickdraw

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oosei25/README.md

Hi, I'm Ofosu 👋🏾

Portfolio • GitHub • GitLab • LinkedIn

Open to collaboration

🎯 About me

I build practical, end-to-end data solutions: from data engineering and analysis to modeling, experimentation, and interactive apps.
I enjoy turning messy data into clear decisions—and shipping work that’s easy for others to use.

🚀 Featured Projects

A few highlights — see my pinned repos for the most current work.

  • A/B Testing + Causal Uplift Dashboard (Streamlit) — Live Demo
    Experiment health, lift, uplift modeling/HTE, and targeting simulation.

  • Marketing Attribution Simulator (Shiny) — Live Demo
    Interactive attribution modeling + scenario testing.

  • Africa Momentum Index (Streamlit) — Live Demo
    Analytics MVP for exploring indicators and trends across African markets.

  • Energy Forecasting (RNNs)
    Time-series forecasting experiments + clean evaluation reporting.

📈 GitHub Stats

Built with ❤️ — simple, focused, and shippable.

Pinned Loading

  1. seaborn_objects_recipes seaborn_objects_recipes Public

    seaborn_objects_recipes is a Python package that extends the functionality of the Seaborn library, providing custom recipes for enhanced data visualization. This package includes below features to …

    Python 11 1

  2. africa-momentum-index-demo africa-momentum-index-demo Public

    A small, production-friendly pipeline + Streamlit dashboard capturing African startup momentum via RSS/Google News and optional X/Twitter (Snscrape). With PostgreSQL (Neon-ready) storage and daily …

    Python 2

  3. rbac-rag-assistant-demo rbac-rag-assistant-demo Public

    End-to-end retrieval-augmented generation (RAG) assistant with role-based access control (RBAC) demo. Highlights a Streamlit front-end, FastAPI service layer, and Qdrant/Chroma index. Demonstrates …

    Python 3

  4. bank-customer-churn bank-customer-churn Public

    Churn risk modeling demo: clean messy bank data, train ML models (Logistic Regression, Random Forest, XGBoost), and explain predictions with SHAP at both global and local levels.

    Jupyter Notebook 3

  5. energy-rnn-forecasting energy-rnn-forecasting Public

    A PyTorch-based, end-to-end energy time-series forecasting demo with LSTM/GRU models, strong seasonal baselines, residual learning, and a CLI for reproducible train/eval runs on OPSD (with run logg…

    Jupyter Notebook 3

  6. marketing-attribution-shiny marketing-attribution-shiny Public

    An interactive Shiny app for multi-touch attribution (Markov) and a lightweight MMM-style ROI scenario simulator (MMM-lite)—with Diagnostics and Data Health checks to make results easier to trust a…

    R 3