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/Aarogya-Heatmap

The vision of our project is - "minimizing the casualties". Basically the USP of our project is to create a heatmap of areas where there is an outbreak of a particular disease. This would help the government to roll out the prevention methods much more fast than the earlier lazy process they use. This would also help people to know about what precautions to take when going to a place with an outbreak (for work and such). We have used and modified the HyperTrack API to generate heatmaps of the areas of outbreak. The second purpose of our app is : Easy access with a professional doctor for treatment to some extent. Very helpful in remote areas where there is a huge lack of good doctors. We have used firebase and created a whole new app for this chat service between the doctor and the patient. Moreover we have created website using html,css and d3.js to showcase our project.

wsheffel/Art_Photography_Website

Website written in HTML5, CSS3, Javascript (Node.js) with D3.

wsheffel/community-groupby

This repository contains notebook + code for DataCamp community post on groupbys, split-apply-combine and pandas.

wsheffel/d3

Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:

wsheffel/DCMetroMetrics

DC Metro Metrics is a project dedicated to collecting and sharing publicly available data related to the DC WMATA Metrorail system.

wsheffel/deepwater

Deep Learning in H2O using Native GPU Backends

wsheffel/economic-indicators-3D-world

I created a 3D version of the world using D3.js. Go the the website link below and pick options from both drop-down menus. The globe on the website can be clicked and dragged if all of the directions on the website are followed. Have fun! :)

wsheffel/enthraler-c3-chart

Create interactive charts to embed in your website. Uses enthral.js, c3.js and d3.js

wsheffel/f1stats

Formula 1 Stats website with interactive D3 JS Graphs

wsheffel/fft_algorithmic_analysis

Srishty Saha UMBC

wsheffel/GloVe

GloVe model for distributed word representation

wsheffel/h2o-tutorials

Tutorials and training material for the H2O Machine Learning Platform

wsheffel/IoT_Hackathon

StayFocussed - a reactive environment IoT solution enhancing productivity by suggesting breaks based on live scoring, developed during the 2017 HHZ IoT hackthon

wsheffel/keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano.

wsheffel/landsatNDSI

Normalized Difference Snow Index Calculator using Landsat Imagery

wsheffel/learn-gremlin

Sample scripts for learning Apache Gremlin graph queries

wsheffel/macgyver

DevOps Multi-Tool https://circleci.com/dashboard

wsheffel/machine-learning

Content for Udacity's Machine Learning curriculum

wsheffel/Machine-Learning-and-Data-Science

ML and DS books and articles

wsheffel/marklogic-business-glossary

A web application for managing business glossaries using MarkLogic.

wsheffel/MLND_Project-titanic_survival_exploration

wsheffel/Python-for-Financial-Analysis-and-Algorithmic-Trading

https://www.udemy.com/python-for-finance-and-trading-algorithms/

wsheffel/rcloud

Collaborative data analysis and visualization

wsheffel/scikit-learn

scikit-learn: machine learning in Python

wsheffel/spark-csv

CSV data source for Spark SQL and DataFrames

wsheffel/SSTApp_chla

Do you live in the Hatteras area? Are you going fishing? Obviously yes, otherwise you wouldn't still be reading this! You need to know where the best fishing is - use our App to find the optimal temperatures and blue waters for your best fishing action!! Our App extracts monthly mean sea surface temperature and monthly MODIS chlorophyll-a data from the NOAA SWFSC ERDDAP website for the Hatteras coastal region. Reactive plot functions are developed to plot these data in accordance to their attributes, before the data are plotted as per user selections.

wsheffel/stream_2_project

A data-driven frontend and backend website using Flask, D3.js, DC.js and Crossfilter.js. Provides analysis of the 2015/16 premier league

wsheffel/The-Smart-House

In this project, real-time data from varies sensors was recorded and regulated automatically. The data was from temperature, humidity, photoresistor, ultrasound and flame fire detector Sensor. The data transmitted/received through UART and commends through Blue tooth of Simblee. Additionally, data showed on OLED and the graphic displayed on website through D3.JS.

wsheffel/TruInfluence-Data-Visualization-D3.js

D3.js Data Visualization website

wsheffel/ud120-projects

Starter project code for students taking Udacity ud120