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plasmic

Visually build pages and frontends, lightning-fast. Plug into your React codebase. Empower anyone to ship.

player-churn-prediction

playroom

Design with JSX, powered by your own component library.

plot

A concise API for exploratory data visualization

plot-create-react-app-example

An example of Observable Plot with Create React App

plotly-js-material-design-theme

NOT MAINTAINED Plotly.js charts with Material Design theme

plotly.py

The interactive graphing library for Python (includes Plotly Express) :sparkles:

portfolio

My self coded personal website build with React.js

portfolio-1

A Next.js & Material UI portfolio that stylizes markdown files from the GitHub API and Contentful CMS.

pppsw-sql

SQL queries/reports for PPPSW

pr_059

Geisel Library Foot Traffic Data Analysis

predicting-best-business-location

Using Kmeans to Predict Best Business Location

predicting-icu-patient-clinical-deterioration---report

For this project, I used publicly available Electronic Health Records (EHRs) datasets. The MIT Media Lab for Computational Physiology has developed MIMIC-IIIv1.4 dataset based on 46,520 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center of Boston between 2001 and 2012. MIMIC-IIIv1.4 dataset is freely available to researchers across the world. A formal request should be made directly to www.mimic.physionet.org, to gain access to the data. There is a required course on human research ‘Data or Specimens Only Research’ prior to data access request. I have secured one here -www.citiprogram.org/verify/?kb6607b78-5821-4de5-8cad-daf929f7fbbf-33486907. We built flexible and better performing model using the same 17 variables used in the SAPS II severity prediction model. The question ‘Can we improve the prediction performance of widely used severity scores using a more flexible model?’ is the central question of our project. I used the exact 17 variables used to develop the SAPS II severity prediction algorithm. These are 13 physiological variables, three underlying (chronic) disease variables and one admission variable. The physiological variables includes demographic (age), vital (Glasgow Comma Scale, systolic blood pressure, Oxygenation, Renal, White blood cells count, serum bicarbonate level, blood sodium level, blood potassium level, and blood bilirubin level). The three underlying disease variables includes Acquired Immunodeficiency Syndrome (AIDS), metastatic cancer, and hematologic malignancy. Finally, whether admission was scheduled surgical or unscheduled surgical was included in the model. The dataset has 26 relational tables including patient’s hospital admission, callout information when patient was ready for discharge, caregiver information, electronic charted events including vital signs and any additional information relevant to patient care, patient demographic data, list of services the patient was admitted or transferred under, ICU stay types, diagnoses types, laboratory measurments, microbiology tests and sensitivity, prescription data and billing information. Although I have full access to the MIMIC-IIIv1.4 datasets, I can not share any part of the data publicly. If you are interested to learn more about the data, there is a MIMIC III Demo dataset based on 100 patients https://mimic.physionet.org/gettingstarted/demo/. If you are interested to requesting access to the data - https://mimic.physionet.org/gettingstarted/access/. Linked repositories: Exploratory-Data-Analysis-Clinical-Deterioration, Data-Wrangling-MIMICIII-Database, Clinical-Deterioration-Prediction-Model--Inferential-Statistics, Clinical-Deterioration-Prediction-Model--Ensemble-Algorithms-, Clinical-Deterioration-Prediction-Model--Logistic-Regression, Clinical-Deterioration-Prediction-Model---KNN © 2020 GitHub, Inc.

pregnancyalgorithm

presentation-aug-26-2021-11-39-06

select * from users

prettyglm

prismr

:art: PrismJS for RMarkdown HTML

project-3-ml-classification

Project was used to build evaluate Framingham Heart Data to classify if an individual is risk prone to coronary heart disease.

project-week-8-final-project

prophet_explore

Shiny App that offers an interactive interface to explore the main functions of the [prophet Package](https://cran.r-project.org/package=prophet)

psyplot

Python package for interactive data visualization

publii-bootstrap-starter

A Bootstrap 4 starter theme for Publii, a world-class CMS for static websites

pushy

Pushy is a responsive off-canvas navigation menu using CSS transforms & transitions.

py

Repository to store sample python programs for python learning

py-cra

python tool to create a react frontend web app for django or flask backend

pycaret

An open-source, low-code machine learning library in Python

pycon-2019-tutorial

Data Science Best Practices with pandas

pycon-pandas-tutorial

PyCon 2015 Pandas tutorial materials

pydqc

python automatic data quality check toolkit

pyecharts

🎨 Python Echarts Plotting Library