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Achievements

Achievement: Pull Shark

Achievements

Achievement: Pull Shark

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stelladeecoder/README.md

Hi, I'm Dee 👋

I'm a data professional and M.S. Data Analytics & Big Data candidate with experience in fraud operations, quality assurance, business intelligence, and data driven process improvement.

I use data to identify patterns, solve business problems, and support better decisions.

Technical Skills

Languages: Python, SQL, R
Data Science: Machine Learning, EDA, Data Cleaning, Regression, NLP
Visualization & BI: Power BI, Looker, Matplotlib, Excel
Tools: Pandas, NumPy, scikit learn, Git, GitHub, Jupyter
Currently Learning: PySpark, Apache Spark, Databricks, AWS, Azure

Featured Projects

Credit Card Fraud Detection

Built a Gradient Boosting model to identify potentially fraudulent credit card transactions and evaluate real world classification tradeoffs.

View Project

Customer Targeting Marketing Analysis

Analyzed customer behavior, spending, and campaign response patterns to identify groups more likely to engage with marketing efforts.

View Project

Customer Purchase & Demographic Analysis

Used Excel, descriptive statistics, and regression analysis to identify factors associated with customer purchase amounts.

View Project

UCB Admissions Explorer

Built and deployed an interactive R Shiny dashboard for exploring admissions outcomes across departments and gender groups.

View Project

Background

My professional experience includes fraud investigations, quality assurance, technical support, dashboard reporting, root cause analysis, and cross functional collaboration with Product and Engineering teams.

I'm currently expanding my work in machine learning, big data, and cloud technologies while completing my graduate degree.

Connect With Me

LinkedIn
Email

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  1. credit-card-fraud-detection-gbm credit-card-fraud-detection-gbm Public

    Built a Gradient Boosting fraud detection model to distinguish fraudulent from legitimate credit card transactions and evaluate real-world classification tradeoffs.

    Jupyter Notebook

  2. customer-targeting-marketing-analysis customer-targeting-marketing-analysis Public

    Analyzed customer behavior and campaign response patterns to identify which characteristics were most associated with successful marketing outcomes.

    Jupyter Notebook

  3. customer-purchase-demographic-analysis customer-purchase-demographic-analysis Public

    Analyzed customer spending patterns in Excel using descriptive statistics, confidence intervals, and regression modeling to identify key drivers of purchase amount.

  4. ucb-admissions-shiny-app ucb-admissions-shiny-app Public

    Built and deployed an interactive R Shiny dashboard to compare UC Berkeley admissions outcomes by department and gender.

    R