class AbdallahHashad:
role = "Machine Learning Engineer & Data Scientist"
location = "Cairo, Egypt 🇪🇬"
edge = "CFA-level valuation + DCF modelling + professional appraisal work"
def building(self):
return ["end-to-end ML pipelines", "leakage-proof feature engineering",
"tuned gradient boosting models", "quantitative finance tooling"]
def current_focus(self):
return {"learning": ["Hands-On ML (Géron)", "Linear Algebra (Strang)", "SQL"],
"goal": "models that answer real economic questions, not just minimise loss"}🌐 Portfolio: abdallah-hashad.vercel.app • 📊 Kaggle: abdallahhashad0 • ✍️ Medium: @abdallahhashad029
| Project | What it does | Stack |
|---|---|---|
| 🏠 moscow-real-estate-price-prediction | 0.98 R² CV — CatBoost / XGBoost / LGBM ensembles, SHAP analysis | Python CatBoost SHAP |
| 🌾 food-price-inflation-analysis | ML pipeline predicting global food price shocks (1990–2024) | Python LGBM Power BI |
| 🏡 airbnb-revenue-prediction | Revenue model on 90k+ EU listings, incl. leakage detection | Python CatBoost |
| 🩺 Diabetes-classification | 6 classifiers with tuned sklearn pipelines | Python scikit-learn |
| 🎓 student-performance-prediction | Score regression with sklearn pipelines | Python scikit-learn |
