wsheffel

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/AiKit-code-samples

wsheffel/arl-presentations

Presentations for the AI Racing League

wsheffel/Best-websites-a-programmer-should-visit

:link: Some useful websites for programmers.

wsheffel/django-registration

Django-registration (redux) provides user registration functionality for Django websites.

wsheffel/donkeycar

Open source hardware and software platform to build a small scale self driving car.

wsheffel/dos-advanced-excel

Advanced Excel and Power BI Training

wsheffel/DP-200-Implementing-an-Azure-Data-Solution

wsheffel/ecosys

TigerGraph Ecosystem

wsheffel/foodkg.github.io

wsheffel/gsql_client

wsheffel/Health-Insurance-Claims-EDA

Detailed EDA on Health Insurance Claims dataset; visualization using Seaborn library.

wsheffel/iPath

iPath is a javascript library for scripting reactive vector graphics, usable as SVG in your website or as DXF for CNC templates.

wsheffel/learn-react

Dan's labs on learning react

wsheffel/Machine-Learning-Study-Materials

Books for ML and Data Science

wsheffel/MachineLearningNotebooks

Python notebooks with ML and deep learning examples with Azure Machine Learning | Microsoft

wsheffel/Mars-EMS

This is an EMS [employee management system] build using the MERN stack.

wsheffel/nlp-tutorial

Natural Language Processing Tutorial for Deep Learning Researchers

wsheffel/PyStrategies

Framework for algorithmic trading strategy development

wsheffel/pyTigerGraph

Python Wrapper for TigerGraph Database

wsheffel/Reactive-website-UI

wsheffel/reactivePortfolio

I have a semi-dynamic HTML\ CSS (.scss)\ JS portfolio website. -This is the React version of the same site.

wsheffel/SeeCodeRun

Live pastebin for JavaScript

wsheffel/similarity

Notebook on doing similarity calculations

wsheffel/StackOverflow_User_Behavioural_Logging

Designed a full stack web application using Bootstrap, Nodejs and MongoDB, which allowed users to create account and view their user interaction behavior patterns on the StackOverflow Website. This implementation involved persistent data logging, use of D3.js and google charts for showing visualization related to user behavior trends and integrating chrome extension with server-side script.

wsheffel/tigergraph-icon-loader

Tools to transform and load many icons into TigerGraph for use in GraphStudio

wsheffel/web-starter-kit

Web Starter Kit - a workflow for multi-device websites

wsheffel/website-4

Introductory website for The Algorithms

wsheffel/website-8

Pattern Lab's website: patternlab.io

wsheffel/website-9

First iteration of our web site will live on readthedocs...

wsheffel/website-scraper-puppeteer

Plugin for website-scraper which returns html for dynamic websites using puppeteer