- CervicalCancerImages_EDA.ipynb:
- RiskFactorEDA.ipynb: Julia's initial work with the tabular dataset from Kaggle. Plan to continue working with it on Wednesday. Data sourced from the kaggle dataset linked in project goals: https://www.kaggle.com/code/mlalasa2170277/rf-cervical-cancer-classification-99-acc
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npi_filtering.ipynb: Initial exploration and filtering of raw NPI data. Data is filtered from 7.6 million entries down to ~400k and from 330 columns down to 32. Resulting data includes only physicians actively practicing in the US who could be reasonably expected to perform pap smears (i.e. OB/GYNs, Family, and Internal Medicine). Note: Raw data for the NPI Registry can be found here: https://download.cms.gov/nppes/NPI_Files.html
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prepare_database.ipynb: This notebook sets up batch geocoding scripts, merging the resulting data, and column manipulations for formatting, aggregations, and more descriptive content. At the end, the prepared data is pushed into a PostgreSQL database table.
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geospatial_queries.ipynb: Testing geospatial queries and features for the streamlit app.
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streamlit_test.py: Minimal streamlit app containing all functionality for the find a provider feature. Includes functionality to search by address or search by current location, and outputs an interactive map and table.