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patient-outliers

To model a group of insured that have a high chance of incurring multiple claims on their health insurance policy. Random Forest, Logical Regression Naïve Bayes were used for modeling.

plotly-dashboards-with-dash

This is the repo for the Udemy Course Python Dashboards with Plotly's Dash

plotly.js

Open-source JavaScript charting library behind Plotly and Dash

pneumonia-detection

Using pretrained ResNet for pneumonia detection

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.

presentations

Presentations

programmingassignment2

Repository for Programming Assignment 2 for R Programming on Coursera

project-data-science-job-outlook

In this project, we discussed the data scientist job market in the Austin, TX area. What are the requirements to be hired, who are the big players in the industry, what are the skills and education demanded the most. The data was scraped from Indeed website and collected information of 7,000 data scientist jobs in the US. Data was organized with Python Pandas, data mining was done in the job description texts to determine job skills, education, experience, companies, and cities. Data was deployed to Sqlite. Other apps and libraries used: JS D3, Numpy, Plotly, Sqlalchemy, Flask, Click, Gunicorn, Jinja2, Markupsafe and Tableau.

public

Public data, demos, software & documents from John Snow Labs.

pubmed-text-mining-tool

A Simple Text Mining Tool for Analyzing Research Paper Abstracts

pycon

Website for PyCon

pystrategies

Framework for algorithmic trading strategy development

python

python-algorithmic-trading-cookbook

Python Algorithmic Trading Cookbook, published by Packt

python-data-mining-quick-start-guide

Python Data Mining Quick Start Guide, Published by Packt

python-for-financial-analysis-and-algorithmic-trading

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

python-tutorials

python_cartography_tutorial

A tutorial about making maps in python using folium.

python_folium_example

Example showing how to generate a map with markers, custom markers, circle markers, vega visualizations, Geojson and choropleth maps

pytigergraph

Python Wrapper for TigerGraph Database

pytorchnlpbook

Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://amzn.to/3JUgR2L

rcloud

Collaborative data analysis and visualization

react-d3

Modular React charts made with d3.js https://reactiva.github.io/react-d3-website/

reactive-website

reactive website- making individual letters respond to mouse movements.,

reactive-website-ui

reactiveportfolio

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

regression_in_insurance

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

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.

rtd-test

Test of Read the Docs Book building

s3cogstack