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/3d-network-graph-visualization

wsheffel/chime

COVID-19 Hospital Impact Model for Epidemics

wsheffel/COVID-19

Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE

wsheffel/covid-19-data

An ongoing repository of data on coronavirus cases and deaths in the U.S.

wsheffel/COVID-20

COVID-19 Detector from x-rays using Computer Vision and Deep Learning

wsheffel/COVID-CT

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

wsheffel/COVID-Net

COVID-Net Open Source Initiative

wsheffel/COVID-QA

API & Webapp to answer questions about COVID-19. Using NLP (Question Answering) and trusted data sources.

wsheffel/covid19_scenarios

Models of COVID-19 outbreak trajectories and hospital demand

wsheffel/covid19india-react

📊 Source code of the main website

wsheffel/covid19model

Code for modelling estimated deaths and cases for COVID19.

wsheffel/covidify

Covidify - corona virus report and dataset generator for python 📈

wsheffel/Fetching-Financial-Data

Fetching financial data for technical & fundamental analysis and algorithmic trading from a variety of python packages and sources.

wsheffel/Fraud-Dashboard

wsheffel/Health_Analytics_Sample_Code

A collection of some analysis done with with medical claims data from my class

wsheffel/Health_Insurance_Data_Analysis_and_Prediction

Predicting health insurance costs(using linear regression) and Grouping health insurance claims(using logistic regression)

wsheffel/Healthcare-Data-Mining-Projects

Data mining projects include predicting risk score of chronic diseases with NHANES data and analysis of patient and insurance claim data.

wsheffel/hvplot

A high-level plotting API for pandas, dask, xarray, and networkx built on HoloViews

wsheffel/medcat-cogstack-workshop

wsheffel/moai

:moyai: Pharma and healthcare competitive intelligence through product website FDA OPDP update frequency.

wsheffel/netflix-titles-dataset

Exploratory Data Analysis on Netflix Movies and TV Shows

wsheffel/pneumonia-detection

Using pretrained ResNet for pneumonia detection

wsheffel/Regression_in_Insurance

This repo is for a logistics regression problem using an Insurance Company as case study

wsheffel/rentorbuy

A Project that uses Zillow research data on Quandl, Prophet for time series forecasting, Altair for vega-lite charts and Folium for an creating interactive map.

wsheffel/stock-analysis-engine

Backtest 1000s of minute-by-minute trading algorithms for training AI with automated pricing data from: IEX, Tradier and FinViz. Datasets and trading performance automatically published to S3 for building AI training datasets for teaching DNNs how to trade. Runs on Kubernetes and docker-compose. >150 million trading history rows generated from +5000 algorithms. Heads up: Yahoo's Finance API was disabled on 2019-01-03 https://developer.yahoo.com/yql/

wsheffel/stock_cnn_blog_pub

This project is a loose implementation of paper "Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach"

wsheffel/streamlit-observable

Embed Observable notebooks into Streamlit apps!

wsheffel/Team-Peony-Primer

Primer for BIME 535

wsheffel/world-atlas

Pre-built TopoJSON from Natural Earth.

wsheffel/yellowbrick

Visual analysis and diagnostic tools to facilitate machine learning model selection.