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NnekaAsuzu/README.md

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About Me

I am a Data Scientist with experience applying machine learning and statistical modeling to solve business problems. My work focuses on exploratory data analysis, feature engineering, model development, evaluation, and communicating insights through dashboards and visualizations that support data-driven decision-making.

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 Regression, Classification, Clustering, XGBoost, Random Forest
Statistics & Analytics Exploratory Data Analysis (EDA), Feature Engineering, Statistical Analysis, A/B Testing
Visualization & BI Power BI, Tableau, Plotly Dash, Matplotlib
Cloud & Tools Git/GitHub, Azure Machine Learning, AWS, Jupyter Notebooks

Projects

These projects demonstrate practical applications of machine learning, statistical analysis, and data visualization to solve business problems. They showcase my approach to exploratory data analysis, feature engineering, model development, evaluation, and communicating actionable insights for data-driven decision-making. See more details on my Portfolio Website!

Featured Machine Learning Projects

Domain Project Machine Learning Project Output Purpose Repository
Finance & Risk Analytics Credit Risk Prediction Model Logistic Regression, Random Forest, XGBoost Azure ML (project deployment), Power BI Dashboard Predict loan default risk to support credit risk assessment and lending decisions Go to Repo
Operations & Supply Chain Supply Chain Risk Prediction & Inventory Optimization Logistic Regression, XGBoost, Azure AutoML Azure ML (project deployment), Power BI Dashboard Predict supplier delivery risk and support inventory planning Go to Repo
Customer Analytics Customer Value & Lifecycle Modeling K-Means Clustering, PCA, XGBoost (CLV), A/B Testing Simulation Streamlit Application Segment customers, estimate Customer Lifetime Value (CLV), and evaluate retention strategies Go to Repo

Additional Applied Data Science Projects

Domain Project Machine Learning Project Output Purpose Repository
Healthcare Operations Healthcare Resource Forecasting ARIMA, Random Forest, Scenario Simulation Streamlit Application Analyze patient demand patterns and evaluate staffing scenarios Go to Repo
Public Health Analytics Toronto Infection Outbreak Analysis Prophet, Trend Analysis Power BI Dashboard Analyze outbreak trends and support public health decision-making Go to Repo
Financial Analytics Financial Forecasting & Scenario Analysis Linear Regression, Random Forest, Prophet Flask Application Explore revenue forecasting and scenario analysis using interactive predictions Go to Repo
Applied Machine Learning โ€ข Statistical Analysis โ€ข Data Visualization โ€ข Business Insights

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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