Welcome!
I’m Nneka Asuzu, a Data Scientist focused on building machine learning models and end-to-end data-driven systems that support real-world decision-making.
About Me
I build machine learning models that uncover patterns in data and translate them into actionable insights.
My work spans the full data science lifecycle from data preparation and feature engineering to model evaluation and deployment, ensuring solutions are practical, scalable, and decision-oriented.
Core Focus Areas
- Machine Learning: Supervised (classification, regression) and unsupervised learning (clustering, dimensionality reduction) using linear models, tree-based models, and SVMs.
- Model Deployment & ML Systems: End-to-end machine learning workflows, including model deployment to cloud and production environments.
- Data Analytics & Statistical Methods: Exploratory data analysis, hypothesis testing, A/B testing, and time series forecasting.
- Data Visualization & BI: Power BI, Tableau, and Plotly Dash.
- Tools & Platforms: Python, SQL, Git, Jupyter Notebook, Azure ML, Azure SQL, Power BI.
Achievements & Impact
- Improved model performance over baseline through feature engineering and hyperparameter tuning across multiple projects.
- Built and deployed machine learning models to support real-world decision-making.
- Designed dashboards that reduced reporting and analysis time by up to 30%, improving decision-making efficiency.
- Automated data workflows to improve consistency, reduce manual effort, and enhance data reliability.
Soft Skills
- Communication: Translate machine learning results into clear, actionable insights for decision-making.
- Problem-solving: Apply structured, data-driven thinking to solve business problems.
- Adaptability: Quickly learn and apply new tools, models, and techniques across projects.
- Collaboration: Work effectively with cross-functional teams to deliver data science solutions.
Featured Projects
- Credit Risk Prediction System – Classification (Logistic Regression → Random Forest → XGBoost), SHAP Explainability, Azure ML, Power BI
- Supply Chain Risk & Inventory Optimization – Classification (Logistic Regression → XGBoost → AutoML benchmarking), Azure ML, Power BI
- Customer Value & Lifecycle Modeling – Clustering (K-Means + PCA), Regression (XGBoost for CLV), A/B Testing, Streamlit
- Healthcare Workforce Optimization – Regression and Random Forest models for demand prediction, scenario simulation, and workforce planning under uncertainty