Welcome!
Nneka Asuzu
Data Scientist applying machine learning, statistical analysis, and data visualization to solve business problems and support data-driven decision-making.
About Me
I apply machine learning and statistical techniques to uncover patterns in data and translate analytical findings into actionable business insights.
My work includes exploratory data analysis, data preparation, feature engineering, model development, evaluation, and communicating insights through dashboards and visualizations to support business decision-making.
Core Focus Areas
- Machine Learning: Supervised (classification, regression) and unsupervised learning (clustering, dimensionality reduction) using linear and tree-based models.
- Applied Machine Learning: Data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation.
- Statistical & Analytical Methods: Exploratory data analysis, descriptive statistics, hypothesis testing, A/B testing.
- Data Visualization & BI: Dashboard development and insights communication using Power BI, Tableau, and Plotly.
- Tools & Platforms: Python, SQL, Git, Jupyter Notebook, Azure Machine Learning, AWS (project exposure), Power BI.
Achievements & Impact
- Improved model performance over baseline through feature engineering and hyperparameter tuning across multiple projects.
- Built machine learning models that supported data-driven business decisions across multiple analytical projects.
- Designed dashboards that reduced reporting and analysis time by up to 30%, improving decision-making efficiency.
- Developed reusable analytical workflows that improved consistency and efficiency of data analysis.
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 – Classification (Logistic Regression → Random Forest → XGBoost), SHAP Model 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 Analytics – Regression and Random Forest models for healthcare demand prediction and staffing analysis.