I specialize in transforming complex processes into clear, efficient solutions. Currently, I drive operational excellence as a Digital Press Operator at RR Donnelley, where precision and quality directly impact client success. Alongside this, I’m building a strong foundation in data science, equipping myself with the technical skills to solve problems through data.
What drives me is curiosity — the desire to uncover patterns, improve systems, and bridge the gap between traditional operations and data-driven decision making. I’m eager to connect with professionals in analytics and technology, contribute to data-focused teams, and continue growing into a data science role where I can deliver measurable impact.
Logistic Regression • Decision Tree • Random Forest • XGBoost • LightGBM • NLP (text preprocessing, vectorization, classification)
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Classification pipeline comparing Logistic Regression, Decision Tree, Random Forest, XGBoost, LightGBM, with hyperparameter tuning; optimized F1 and AUC-ROC.
🔗 Repo: README : https://github.com/brooke-holland/Telecom_Churn_Forcast/blob/main/README.md
🔗 PROJECT : https://github.com/brooke-holland/Telecom_Churn_Forcast/blob/main/Telecom_churn.ipynb
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Taxi Data will have data on Taxi Company names. Gathering the trip number amount of rides for each company during the dates of, November 15-16 2017. The Data will have the dropoff locations in the Chicago neighborhoods, the average number of rides and weather conditions during these trips. Testing Hypothesis using scipy, SQL
🔗 Repo: README : https://github.com/brooke-holland/SQL.Zuber_Taxi.Rides
🔗 PROJECT : https://github.com/brooke-holland/SQL.Zuber_Taxi.Rides/blob/main/SQL_Zuber.Taxi.Project.ipynb
- Email: brooke.b.holland@gmail.com
- LinkedIn: www.linkedin.com/in/brooke-holland
- GitHub: https://github.com/brooke-holland