Skip to content
View brock-green's full-sized avatar

Block or report brock-green

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
brock-green/README.md

Hi there 👋

Brock Green | Data Scientist | Bridging the Gap Between Data & Action About Me:

Highly motivated and results-oriented Data Scientist with proven experience across the full data science pipeline. Adept at translating complex insights into actionable solutions for diverse stakeholders, including both technical and non-technical audiences. Skilled in data acquisition, preparation, exploration, modeling, and storytelling. Possess strong stakeholder management experience, ensuring clear communication and project success. Enthusiastic about leveraging data to drive positive impact.

Technical Skills:

Python | SQL | Tableau | Matplotlib | MySQL | Pandas | NumPy | Spark Machine Learning | Natural Language Processing | Data Storytelling Classification | Regression | Clustering | Time Series Analysis | Anomaly Detection Seaborn | Scikit-learn | Anaconda | Jupyter Notebooks | Git/GitHub

Check out my pinned repositories to see these projects!:

  • Smart Moves: Guiding Your Way Home: Interactive dashboard leveraging unsupervised learning to help families choose the best location based on affordability, crime, schools, and commute (Python, Pandas, NumPy, Scikit-learn, Tableau).

  • Natural Language Processing and Classification: Predicting main programming language from Github repository READMEs (Python, Pandas, NumPy, RegEX, Classification, BeautifulSoup, Web-Scraping).

  • Wine Quality Prediction: Using KMeans clustering and statistical analysis to build a KNN model for predicting wine quality (Python, Pandas, NumPy, SQL, Scikit-learn, matplotlib, seaborn).

  • Customer Churn Analysis: Identifying customer churn drivers with statistical analysis and building a Logistic Regression model for prediction (Python, Pandas, Tableau, SQL, Scikit-learn, matplotlib, seaborn).

Pinned Loading

  1. Jay-Almendarez/Smart-Moves Jay-Almendarez/Smart-Moves Public

    Jupyter Notebook

  2. Individual-Project-Fraud-Detection Individual-Project-Fraud-Detection Public

    Jupyter Notebook

  3. classification-project classification-project Public

    Jupyter Notebook

  4. regression-project regression-project Public

    Jupyter Notebook

  5. jeremiah-toribio/wine-clusters jeremiah-toribio/wine-clusters Public

    Jupyter Notebook 1

  6. jeremiah-toribio/1800-readmes jeremiah-toribio/1800-readmes Public

    Jupyter Notebook