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/6-25-2015-Statistical-Programming-DC-master
wsheffel/bokeh-tutorial-ipynb
Bokeh tutorial in IPython Notebooks
wsheffel/byob-presentations
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wsheffel/Collection-of-Website-Works
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wsheffel/CS573-final-Project
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wsheffel/CSE331
CSE 331: Software Design and Implementation (taught by Michael Ernst)
wsheffel/CSE473
CSE 473: Introduction to Artificial Intelligence (taught by Rajesh Rao)
wsheffel/davidjamesknight
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wsheffel/dc-budget-treemap
Treemap for DC Budget in D3; obviously inspired by Mike Bostock
wsheffel/dga_predict
wsheffel/GraphicDen
A website that parses a spreadsheet and generates a graph for analysis (support for Bar, Pie and Line Graph). Built using Django and D3.JS.
wsheffel/IPython-plotly
A collection of data science IPython notebooks with Plotly graphs
wsheffel/iTrade
Large Database example. A website that allows users to simulate playing the stock market. Includes brokers, funds, real world stock values and reactive stock values
wsheffel/map_inside
wsheffel/mfca
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wsheffel/PalatnickAspyn-DPDevProject
wsheffel/ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
wsheffel/public
Public data, demos, software & documents from John Snow Labs.
wsheffel/PubMed-Text-Mining-Tool
A Simple Text Mining Tool for Analyzing Research Paper Abstracts
wsheffel/react-d3
Modular React charts made with d3.js https://reactiva.github.io/react-d3-website/
wsheffel/sec-paper
awesome security paper
wsheffel/umd-byob-site
UMD BYOB Website
wsheffel/VisualizingTheLeague
A node.js & d3 based website for Interactive Data Visualization @ RPI.
wsheffel/world.geo.json
Annotated geo-json geometry files for the world