Welcome!
Nneka Asuzu
Data Scientist focused on building machine learning models and data-driven solutions for 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 learning (classification, regression) and unsupervised learning (clustering, dimensionality reduction) using tree-based models, linear models, and kernel methods.
- Applied Machine Learning: Data preprocessing, feature engineering, model training, and evaluation using structured machine learning workflows.
- Statistical & Analytical Methods: Exploratory data analysis, hypothesis testing, A/B testing, and time series forecasting.
- Data Visualization & BI: Dashboard development and insights communication using Power BI, Tableau, and Plotly.
- Tools & Platforms: Python, SQL, Git, Jupyter Notebook, Azure ML, Power BI.
Achievements & Impact
- Improved model performance over baseline through feature engineering and hyperparameter tuning across multiple projects.
- Built machine learning models to support data-driven decision-making and predictive analytics workflows.
- Designed dashboards that reduced reporting and analysis time by up to 30%, improving decision-making efficiency.
- Automated data processing workflows to improve reliability and consistency of analytics outputs.
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