Pinned Repositories
42-CFR
Random scripts and work
hospital-chargemaster
hospital chargemaster lists for open source healthcare
nexmon_csi
Channel State Information Extraction on Various Broadcom Wi-Fi Chips
predicting-Paid-amount-for-Claims-Data
Introduction The context is the 2016 public use NH medical claims files obtained from NH CHIS (Comprehensive Health Care Information System). The dataset contains Commercial Insurance claims, and a small fraction of Medicaid and Medicare payments for dually eligible people. The primary purpose of this assignment is to test machine learning (ML) skills in a real case analysis setting. You are expected to clean and process data and then apply various ML techniques like Linear and no linear models like regularized regression, MARS, and Partitioning methods. You are expected to use at least two of R, Python and JMP software. Data details: Medical claims file for 2016 contains ~17 millions rows and ~60 columns of data, containing ~6.5 million individual medical claims. These claims are all commercial claims that were filed by healthcare providers in 2016 in the state of NH. These claims were ~88% for residents of NH and the remaining for out of state visitors who sought care in NH. Each claim consists of one or more line items, each indicating a procedure done during the doctor’s visit. Two columns indicating Billed amount and the Paid amount for the care provided, are of primary interest. The main objective is to predict “Paid amount per procedure” by mapping a plethora of features available in the dataset. It is also an expectation that you would create new features using the existing ones or external data sources. Objectives: Step 1: Take a random sample of 1 million unique claims, such that all line items related to each claim are included in the sample. This will result in a little less than 3 million rows of data. Step 2: Clean up the data, understand the distributions, and create new features if necessary. Step 3: Run predictive models using validation method of your choice. Step 4: Write a descriptive report (less than 10 pages) describing the process and your findings.
PyTorchNLPBook
Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://amzn.to/3JUgR2L
wsheffel's Repositories
wsheffel/nexmon_csi
Channel State Information Extraction on Various Broadcom Wi-Fi Chips
wsheffel/BudgetKey
Opening the Israeli Budget!
wsheffel/budgetkey-data-pipelines
Budget Key data processing pipelines
wsheffel/budgetkey-k8s
Budgetkey Kubernetes Environment
wsheffel/CogStack-SemEHR
Surfacing Semantic Data from Clinical Notes in Electronic Health Records for Tailored Care, Trial Recruitment and Clinical Research
wsheffel/cortex
Scale compute-intensive serverless workloads
wsheffel/design-resources-for-developers
Curated list of design and UI resources from stock photos, web templates, CSS frameworks, UI libraries, tools and much more
wsheffel/geemap
A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and folium
wsheffel/Hands-On-Machine-Learning-for-Algorithmic-Trading
Hands-On Machine Learning for Algorithmic Trading, published by Packt
wsheffel/Healthcare-Claims-analysis
Analysis of comprehensive healthcare reforms and amendments which addresses healthcare insurance, costs, and preventive care after ACA Law of US Dept of Health
wsheffel/HealthcareClaims
JSS Hackathon
wsheffel/interactive_climate_map
Tutorial for making interactive, choropleth maps of temperature data in Folium, Plotly, and Bokeh.
wsheffel/jupyter
Jupyter metapackage for installation, docs and chat
wsheffel/Machine-Learning-for-Algorithmic-Trading-Bots-with-Python
wsheffel/machine-learning-for-trading
Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.
wsheffel/Mastering-Geospatial-Analysis-with-Python
Mastering Geospatial Analysis with Python, published by Packt
wsheffel/mordecai
Full text geoparsing as a Python library
wsheffel/neomap
A Neo4j Desktop application to visualize nodes with geographic attributes on a map.
wsheffel/nodes2021_kg_workshop
wsheffel/Plotly-Dashboards-with-Dash
This is the repo for the Udemy Course Python Dashboards with Plotly's Dash
wsheffel/Python-Algorithmic-Trading-Cookbook
Python Algorithmic Trading Cookbook, published by Packt
wsheffel/Python-Data-Mining-Quick-Start-Guide
Python Data Mining Quick Start Guide, Published by Packt
wsheffel/Python-Tutorials
wsheffel/s3cogstack
wsheffel/StockSentimentTrading
Algorithmic Trading using Sentiment Analysis on News Articles
wsheffel/streamlit-folium
Streamlit Component for rendering Folium maps
wsheffel/synthea
Synthetic Patient Population Simulator
wsheffel/tg-ecosys-docs
wsheffel/us-atlas
Pre-built TopoJSON from the U.S. Census Bureau.
wsheffel/Using-scispaCy-for-Named-Entity-Recognition
A beginner's guide to using Named-Entity Recognition for data extraction from biomedical literature