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Achievement: QuickdrawAchievement: Pull Sharkx2Achievement: YOLO

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claireboyd/README.md

Hi there 👋

My name is Claire Boyd and I am currently a data scientist with NYC's Department of Finance Property Modeling team, where we assess the value of all residential and commercial properties in the city annually. I am a recent graduate of UChicago's Masters in Computational Analysis and Public Policy program, where I gained a strong foundation in several programming languages, including Python, R, SQL/HQL, HTML, and CSS, as well as experience with IDEs like Visual Studio Code, IntelliJ and Tableau Public.

Claire's GitHub stats

Feel free to explore the following projects/coursework below to get a better sense of my skills and interests:

  • 🍽️ Dirty Comments, Clean Plates (March 2024): Used a corpus of text-based Yelp restaurant reviews to train a model to classify if a restaurant is likely to fail a health inspection and predict if a review is human-generated or generated by OpenAI's GPT 3.5 or 4.

  • ☎️ 311 Requests in Chicago (December 2023): Created a simple web app which gives users a summary of the 311 requests in their Chicago neighborhood, built with Lambda Architecture principles using Apache's tech stack (HDFS, Hadoop, Hive, Spark, etc). The cluster that the app was built with is no longer active, so watch the video included to see the app in action!

  • 🔌 Forecasting Electricity Usage (December 2023): Used time series modeling (Regression with ARIMA errors and ELS) to model and forecast monthly residential electricity usage in California. All code was written in R and all analysis is summarized in the final presentation deck.

  • 🐀 Predicting Neighborhood-Level Rat Activity in New York City (September 2023): Created a time series predictive model to forecast the volume of weekly rat-related 311 requests in each neighborhood, informing mitigation strategy for NYC Rat Czar.

  • 🏠 Finding comparable properties with LightGBM (June 2023): Developed a new feature, the "comparable finder," into the R package lightsnip which enables accurate identification of comparable properties crucial for assessing property values in Cook County. Read more here for a longer explanation of this project.

  • 🩻 Predicting Pneumonia using Chest X-Ray Images (May 2023): Working alongside three classmates, we used LeNet and Logistic Regression to predict pneumonia using chest X-Ray images with 99.4% recall accuracy, after implementing different image transformations in pytorch!

  • 📈 COVID-19 Online: How people interacted with government websites during the pandemic (March 2023): Working alongside three classmates, we built a complete data pipeline (from collection to visualization) to explore web traffic to HHS websites during the pandemic.

  • 📖 Scraper for Chicago Maps Archive (Feb 2023): In my role as digialization specialist for the University of Chicago Library System Special Collection's Department, I reconfigured data made publicly available by the Chicago History Museum to help process and digitize the maps for public use by University of Chicago Library's Preservation Departments.

  • 📓 CAPP Coursework (Aug 2022-Present): This repository gives a better sense of the programming assignments that I've completed through my coursework to-date that showcase my technical abilities. My code for each assignment listed is available upon request.

  • 📝 Publications (Aug 2018-Present): This repository has a list of my recent publications from my time as a researcher at the Urban Institute's Office of Race and Equity Research.

For more information about any of the above, please feel free to explore my current resume, reach out via email or connect with me on LinkedIn. Looking forward to connecting!

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  1. 311requests_chicago 311requests_chicago Public

    Created a simple web app which gives users a summary of the types of 311 requests in their Chicago neighborhood, built with Lambda Architecture principles using Apache's tech stack

    HiveQL

  2. predicting_rats_nyc predicting_rats_nyc Public

    Used time series modeling of 750K relevant 311 requests to predict the volume of weekly rat activity for every NYC neighborhood, with an average MAPE of 5.6%, which currently feeds into a live Powe…

    Jupyter Notebook 1

  3. silent_p silent_p Public

    Group project for Machine Learning for Public Policy, using LeNet and Logistic Regression for a computer vision binary classification task

    Jupyter Notebook

  4. covid19_online covid19_online Public

    Course project that uses a complete data pipeline (from collection to visualization) contextualizing traffic to HHS websites during the COVID-19 pandemic.

    Python