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/PyTorchNLPBook
Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://amzn.to/3JUgR2L
wsheffel/arl
wsheffel/AutomatedStockTrading-DeepQ-Learning
Every day, millions of traders around the world are trying to make money by trading stocks. These days, physical traders are also being replaced by automated trading robots. Algorithmic trading market has experienced significant growth rate and large number of firms are using it. I have tried to build a Deep Q-learning reinforcement agent model to do automated stock trading.
wsheffel/bokeh_map
Basic interactive map using bokeh and geopandas packages in Python.
wsheffel/BootstrapExample
Example reactive website in HTML5 with CSS3 and most of bootstrap 4 basics.
wsheffel/coding-interview-university
A complete computer science study plan to become a software engineer.
wsheffel/covid
Covid-19 patients' self-monitoring at home via forms sent by SMS
wsheffel/covid19-testing
COVID-19 Testing Site Finder with Risk Assessment
wsheffel/covid19_dashboard
This is a dashboard, similar to the one from Johns Hopkins usind open source technology.
wsheffel/Covidcheck
Open source iOS app to track COVID-19 cases in a beautiful, easy-to-use interface
wsheffel/CovidCrowd
A crowd-sourcing platform for the Covid-19 Pandemic
wsheffel/Data-Journalism-and-Storytelling-with-D3
This website utilizes D3.js and d3-tip.js plugin to plot interactive chart that helps understand trends shaping people's lives from dataset provided by U.S. Census Bureau and the Behavioral Risk Factor Surveillance System.
wsheffel/django-wedding-website
A django-powered wedding website and guest-management system
wsheffel/donkeycar-notebooks
Notebook of tools I have written about the Donkey Car including Jupyter notebooks for doing analysis of the data.
wsheffel/gatsby
Build blazing fast, modern apps and websites with React
wsheffel/iGenomics
App for Mobile DNA Sequence Alignment and Analysis
wsheffel/is-website-vulnerable
finds publicly known security vulnerabilities in a website's frontend JavaScript libraries
wsheffel/jquery
jQuery JavaScript Library
wsheffel/MechanicalSoup
A Python library for automating interaction with websites.
wsheffel/node-website-scraper
Download website to local directory (including all css, images, js, etc.)
wsheffel/python_cartography_tutorial
A tutorial about making maps in python using folium.
wsheffel/TH_COVID19_International
wsheffel/website
Coding Train website
wsheffel/website-1
Kubernetes website and documentation repo:
wsheffel/website-2
Let's Encrypt Website and Documentation
wsheffel/website-3
The code for the Certbot instruction generator and documentation
wsheffel/website-5
ReactiveUI documentation and guidelines website. PR's welcome! 💖
wsheffel/website-6
Kyma landing page with blog, documentation and roadmap
wsheffel/website-7
Website running user and event management.
wsheffel/website-addons
Odoo website addons