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jamiubadmusng/README.md

Jamiu Olamilekan Badmus

Economist • Data Scientist • Research Analyst


About Me

I leverage economic theory and machine learning to analyze trade policy, geopolitical risks, and business problems. My work bridges academic research with practical data-driven solutions.


🎓 Background

  • Erasmus Mundus Master's in Economics of Globalization and European Integration (EGEI) — fully funded by the European Commission, 2023-2025
  • M.Sc. & B.Sc. Economics (First Class & Distinction) — Tai Solarin University of Education, Nigeria
  • Research Intern — United Nations University (UNU-CRIS), Bruges, Belgium — March 2025 to January 2026

🔬 Economics Research

Focus Areas: International Trade • Trade Policy • Geoeconomics • Development Economics

Current research examines the impact of trade policy and geopolitical risks on international trade and investment.

Repositories: Replication packages (usually in R) • Code for published & working papers


🤖 Data Science Projects (by industry)


🛠️ Tech Stack

Python R Stata Scikit-learn Pandas XGBoost GitHub


Bridging economic theory with data-driven insights

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  1. opensource-success-predictor opensource-success-predictor Public

    🔮 Machine learning model predicting GitHub repository success using early activity signals (first 30 days). Achieves 91.3% ROC-AUC on 34K+ repos from GH Archive data.

    Jupyter Notebook 1

  2. customer-churn-prediction customer-churn-prediction Public

    🛒 Machine Learning model to predict customer churn in e-commerce using RFM analysis and LightGBM. Achieved 77% ROC-AUC with 75% recall. Features SHAP interpretability, 24 engineered features from 5…

    Python 1

  3. sdid_gravity_monte_carlo sdid_gravity_monte_carlo Public

    Staggered DID in Structural Gravity Monte Carlo Analysis

    TeX 1

  4. ex-ante-afcfta-replication ex-ante-afcfta-replication Public

    Replication package for Ex-Ante Economic Impacts of the AfCFTA

    R 1

  5. GEPPML-R GEPPML-R Public

    R implementation of General Equilibrium Analysis with PPML by Anderson, Larch, & Yotov, 2018.

    R 1

  6. credit-risk-assessment credit-risk-assessment Public

    Machine learning model to predict loan defaults using the German Credit dataset. Features cost-sensitive classification, SHAP interpretability, and risk segmentation with 80.43% ROC-AUC. Built with…

    Jupyter Notebook 1