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This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
Generates synthetic data and user interfaces for privacy-preserving data sharing and analysis.
An R package to compute WAR for offensive players using nflscrapR
sportsdataverse python package
An R package enabling the computation of openWAR using MLBAM data
A comprehensive set of R functions to access the Survey Solutions REST API (httr based version)
A tool box mostly for CATI surveys in Survey Solutions
College football database - data store for all sorts of data and statistics pertaining to college football
NPM package for retrieving NCAA football data
An R package to quickly obtain clean and tidy college football play by play data
Datasets of daily time-series data related to COVID-19 for over 20,000 distinct locations around the world.
Steps to create electrification estimates from settlement maps and nighttime satellite imagery.
Predicting the winner of college football games using Scikit-learn and data from sports-reference.com
Fit interpretable models. Explain blackbox machine learning.
Text and code for the second edition of Think Bayes, by Allen Downey.
An awesome list of high-quality open datasets in public domains (on-going).
A hour lighting introduction to Python for WBG staff delivered on Data Day on Feb 13
Application for analyzing traffic flows, drawing upon GPS data from multiple sources.
Web application for processing and visualizing traffic statistics.
DRIVER - Data for Road Incident Visualization, Evaluation, and Reporting
Calculate regional ETAs, at scale. Measure accessibility. Calculate impact of road changes.
Various examples for Google Earth Engine in Python using Jupyter Notebook
Interactive, thoroughly customizable maps in the browser, powered by vector tiles and WebGL