I am a team-oriented data scientist who creates relatable stories using machine learning models and visualizations via python to develop insights and help solve problems with data.

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• Querying: PostgreSQL / SQLite
• Coding: Python
• Analysis: Excel / Pandas
• Visualization: Matplotlib / Tableau
• Machine Learning: Sklearn / Tensorflow 2.0
• Big Data: Spark / Big Query
• Workflow: Bash / Git
• GeoSpatial Kepler.gl, Geopandas
• NLP SpaCy, NLTK

• Wine Varietal Predictor: Modeled a 21-class predictor using a convolutional neural net that was able to predict wine varietals 3 times better than the baseline.
• Auto Accident Severity Predictor: Deployed a webapp that predicted auto accident severity as part of a team working on a data set with over 4.2 million observations.
• Social Media Classifier: Extracted 6000+ social media posts from two different Reddit threads and processed them through a Natural Language Processing workflow that used a Voting Classifier model to classify them with a balanced accuracy of over 90% versus a baseline of 27.5%.
• Real Estate Price Predictor: Analyzed a housing dataset with over 82 features to identify relationships and build a predictor model that could determine a housing price within less than 10% of its actual value.
• STEM-Readiness Analysis: Analyzed National and State of California ACT data to find relationships between size and type of school district and the influence on ACT science and math scores.
• Distribution Expansion Analysis: Analyzed demographic information of 6 potential regions to determine the best location for a regional distribution hub.

• Classification Metrics for Beginners
• How to Display a Kepler.gl Map Using Jupyter Notebook
• Interpreting Scattertext: a seductive tool for plotting text