About Me

Data always has a story to tell, but it rarely speaks clearly. It hides behind noise, nulls, and chaos. I don’t settle for the surface narrative, I translate the static. Using Python to clean the mess and SQL to interrogate databases, I push past the obvious ‘what happened’ to find the undeniable ‘why’. I break the data down until it yields a clear, actionable direction. And when the queries are finally optimized, I’m likely applying that same methodical patience to maintaining my sourdough starter or keeping a temperamental basil plant thriving.

Technical Skills

  • Languages: Python, SQL
  • Libraries & Frameworks: Pandas, Scikit-Learn, Matplotlib, Seaborn, FastAPI
  • Data Tools: DBeaver, Jupyter, Git, SteamSpy API
  • Core Competencies: Data Cleaning, Feature Engineering, Predictive Modeling, Exploratory Data Analysis (EDA), Pipeline Deployment

Apple Global Sales Analysis

Tech Stack: SQL, Python (Pandas, Seaborn), Exploratory Data Analysis

Overview: An end-to-end exploratory data analysis of synthetic data regarding historical sales data to identify core revenue drivers and regional market trends.

Key Highlights:

  • Analyzed complex financial datasets to isolate product lifecycle plateaus and growth markets.
  • Translated raw sales metrics into actionable business intelligence and clear visual dashboards.

Steam Hidden Gems: Machine Learning Classifier

Tech Stack: Python (Pandas, Scikit-Learn, Matplotlib, Seaborn), Feature Engineering

Overview: A predictive machine learning pipeline analyzing 10,000 Steam games to identify “hidden gems” by engineering a custom engagement metric and building a binary classifier.

Key Highlights:

  • Engineered a custom quality metric from raw review data to isolate a target class of undiscovered games, utilizing balanced class weights to handle a strict 90/10 dataset imbalance.
  • Identified and successfully mitigated critical data leakage during the feature selection phase, ensuring the model relied purely on independent commercial variables.
  • Evaluated both Random Forest and Logistic Regression models, ultimately proving that linear models generalized better and that commercial strategy (price, discounts) is a statistically weak predictor of community-driven game quality.

Credentials

  • B.S. Data Analytics — Western Governors University, Dec 2025