bcecon Goto Github PK
Name: Byeong-Hak Choe
Type: User
Company: SUNY Geneseo Business
Bio: exonomics phd | theory + data | the environment | climate | applied economics | data science
Twitter: climate_econ
Blog: https://bcecon.github.io
Name: Byeong-Hak Choe
Type: User
Company: SUNY Geneseo Business
Bio: exonomics phd | theory + data | the environment | climate | applied economics | data science
Twitter: climate_econ
Blog: https://bcecon.github.io
Automated political text analysis. The machine learning model is trained on data from the https://manifestoproject.wzb.eu/ and uses bag-of-words features to predict political tendencies.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Using Git and GitHub with R, Rstudio, and R Markdown
Notebooks and code for the book "Introduction to Machine Learning with Python"
Lecture notes for EC 607
source code
Mastering Shiny: a book
R code for Angrist & Pischke Mastering Metrics
Material for a one-week graduate course on mathematical methods for macroeconomics
Material from the Big Data course at Chicago Booth
This tutorial will introduce key concepts in machine learning-based causal inference. This tutorial is used by professor Susan Athey in the MGTECON 634 at Stanford. Scripts were translated into Python.
This tutorial will introduce key concepts in machine learning-based causal inference. This tutorial is used by professor Susan Athey in the MGTECON 634 at Stanford.
Data and Program files for Causal Inference: The Mixtape
Economic Policy Analysis with Overlapping Generations Models (Autumn 2017)
The PIC Math 2022 Workshop on Data Science
Code used to collect news articles, process the text, and train machine learning algorithms to classify each article by their source
Research project to detect political ideology of presidential candidates using their speech. Conducted for Natural Language Processing in Context final exam.
Code from "Introduction to Python for Econometrics, Statistics and Data Analysis" by Kevin Sheppard
Pseudo API for Google Trends
A collection of projects published by Bloomberg's Quantitative Finance Research team.
R for data science: a book
R Markdown: The Definitive Guide (published by Chapman & Hall/CRC in July 2018)
R Markdown Cookbook. A range of tips and tricks to make better use of R Markdown.
A tool for Semantic Scaling of Political Text (branch of Topfish, a suite of tools for Political Text Analysis)
Censored tweets annotated for specificity
Spring 2020 AEM 7130: Dynamic Optimization/Computational Methods
Notes and exercise attempts for "An Introduction to Statistical Learning"
Naive Bayes Classifier implemented using Python-NLTK and Tweepy API.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.