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NnekaAsuzu/README.md

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About Me

I am a Data Scientist specializing in building end-to-end machine learning and statistical modeling solutions. I focus on feature engineering, model development, and evaluation to solve real-world problems using data.

Education & Expertise:

Master of Science (M.S.) in Management and Systems, New York University (NYU): Specialization in Database Technologies, Data Analytics, and Applied Data Science.

I thrive in collaborative environments, delivering data-driven solutions that support decision-making and operational efficiency.

My Technical Toolkit

Category Tools & Technologies
Programming & Data Tools Python (Pandas, NumPy, Scikit-learn), SQL
Machine Learning & Modeling Regression, Classification, Clustering, Time Series (ARIMA/Prophet), XGBoost, Random Forest
Visualization & BI Matplotlib, Seaborn, Plotly Dash, Power BI, Tableau
Experimentation & Analytics A/B Testing, Statistical Analysis, Feature Engineering
Cloud & MLOps Git/GitHub, Azure ML Pipelines, Jupyter Notebooks

Projects

These projects highlight my ability to solve real-world problems using machine learning, statistical modeling, and end-to-end data science workflows. See more details on my Portfolio Website!

Featured Projects (Machine Learning & Decision Systems)

Domain Project Model Type Deployment Purpose Repo
Finance & Risk Analytics Credit Risk Prediction Model Classification (Logistic Regression โ†’ Random Forest โ†’ XGBoost) Azure ML Endpoint + Power BI Dashboard Predict loan default risk and improve credit decisioning Go to Repo
Operations & Supply Chain Supply Chain Risk Prediction & Inventory Optimization Classification (Logistic Regression โ†’ XGBoost โ†’ AutoML Benchmark) Azure ML Managed Endpoint + Power BI Risk Dashboard Predict high-risk suppliers and optimize inventory allocation Go to Repo
Customer Analytics & Marketing Customer Value & Lifecycle Modeling Clustering (K-Means + PCA), Regression (XGBoost for CLV), A/B Testing Simulation Streamlit App Segment customers and predict lifetime value for targeted retention strategies Go to Repo

Supporting Projects (Applied Modeling & Analytics)

Domain Project Model Type Deployment Purpose Repo
Healthcare Operations Healthcare Resource Forecasting Time Series (ARIMA, Random Forest) + Simulation Streamlit App Forecast patient demand and optimize staffing decisions Go to Repo
Public Health Analytics Epidemiology: Toronto Outbreaks Time Series Forecasting (Prophet) + Trend Analysis Power BI Dashboard + ETL Pipeline Detect outbreak trends and support public health planning Go to Repo
Forecasting & Scenario Modeling Financial Forecasting & Scenario API Forecasting (Linear Regression โ†’ Random Forest โ†’ Prophet) Flask API Provide revenue forecasting and scenario simulation Go to Repo
End-to-end machine learning for real-world decision systems

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  1. nnekaasuzu.github.io nnekaasuzu.github.io Public

    Data Scientist building machine learning systems that transform data into actionable, real-world decisions through end-to-end workflows.

    CSS

  2. credit_risk_loan_default_prediction credit_risk_loan_default_prediction Public

    Develop a machine learning system to predict loan default risk using XGBoost and Random Forest, enabling data-driven credit risk assessment and lending decisions.

    Jupyter Notebook

  3. customer_value_lifecycle_modeling customer_value_lifecycle_modeling Public

    Developed an end-to-end customer intelligence pipeline to segment users, predict Customer Lifetime Value (CLV), and evaluate retention strategies using RFM feature engineering, clustering, and supeโ€ฆ

  4. supply_chain_risk_prediction supply_chain_risk_prediction Public

    Built an end-to-end supply chain intelligence pipeline using Azure SQL, feature engineering, and machine learning, benchmarking Logistic Regression, XGBoost, and Azure AutoML to predict supplier riโ€ฆ

  5. healthcare_workforce_optimization healthcare_workforce_optimization Public

    Predict and optimize healthcare staffing requirements under varying patient loads and shift patterns using Random Forest and regression models with scenario simulations