Pinned Repositories
ACA_map
Interactive map hosting embedding
CARES_Act
Data engineering and analysis of CARES Act funding data, initially in service of a partnership between the National Press Foundation and DataKind DC. The work done here will be used to publish articles on the efficacy and various facets of CARES Act (round 1, at least) stimulus funding.
computer_vision
Project code for AI Programming with Python ND Program
conda
Specifying a conda environment with `environment.yml`
disaster-prone-nlp
A project using natural language processing to predict the most relevant category for disaster-related tweets such that emergency aid organizations can direct help efficiently
hospital-chargemaster
Analysis of American hospitals and their quality of care
pytorch-forecasting
Time series forecasting with PyTorch
TXVoterRolls
Import, memory optimization, and basic analysis of Texas voter rolls data from mid-2018
neodash
NeoDash - a Dashboard Builder for Neo4j
VESPID
A project combining network analysis with text-based topic modeling and scientific workforce surveys to identify waxing and waning scientific disciplines and other interesting questions for research.
emigre459's Repositories
emigre459/hospital-chargemaster
Analysis of American hospitals and their quality of care
emigre459/pytorch-forecasting
Time series forecasting with PyTorch
emigre459/ACA_map
Interactive map hosting embedding
emigre459/CARES_Act
Data engineering and analysis of CARES Act funding data, initially in service of a partnership between the National Press Foundation and DataKind DC. The work done here will be used to publish articles on the efficacy and various facets of CARES Act (round 1, at least) stimulus funding.
emigre459/computer_vision
Project code for AI Programming with Python ND Program
emigre459/conda
Specifying a conda environment with `environment.yml`
emigre459/disaster-prone-nlp
A project using natural language processing to predict the most relevant category for disaster-related tweets such that emergency aid organizations can direct help efficiently
emigre459/SolarPrize
Contains code related to various DOE Solar Prizes, including the American-Made Solar Prize. Includes things like assigning judges to submissions based upon keyword similarity scores.
emigre459/Data-Analysis
Data Science Using Python
emigre459/DBCV
Python implementation of Density-Based Clustering Validation
emigre459/deep-learning-v2-pytorch
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
emigre459/docker-neo4j-publish
emigre459/dspytests
emigre459/finding_donors
Udacity Data Scientist Nanodegree Term 1 Project 1
emigre459/full-stack-dev
Handy tools and useful info from my full stack web dev learning journey
emigre459/fuzzymatcher
Record linking package that fuzzy matches two Python pandas dataframes using sqlite3 fts4
emigre459/geopandas
Python tools for geographic data
emigre459/German_Demographics_Clustering
emigre459/GoogleCal-CommuterInvoicing
Generates invoices for different commuters who have signed up to ride in our car using data from a shared Google Calendar
emigre459/green-chains
emigre459/Hospital-Chargemasters
emigre459/multi-output-glucose-forecasting
The code used for the paper Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories published in KDD 2018
emigre459/OSTI-API
emigre459/ReviewerNormalizing
Creates code for taking scores (e.g. as part of a prize competition) and normalizes judges' scores in an attempt to minimize scoring bias and comparing one judge's scores as directly as possible to another judge's scores by mapping them to the same normal distribution.
emigre459/RPPR2-Checker
emigre459/SETO_Staff_Analysis
emigre459/social-lstm
Social LSTM implementation in PyTorch
emigre459/stackoverflow
Findings from Stackoverflow 2017
emigre459/StroopProject
Project looking at results testing the Stroop Effect and measuring the difference in means of a group of participants
emigre459/transformer
Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series.