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causaltree

Working repository for Causal Tree and extensions

codechella

Data, Code and other material for CodeChella concert

datasciencecoursera

datasharing

The Leek group guide to data sharing

dmi-tcat

Digital Methods Initiative - Twitter Capture and Analysis Toolset

econml

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

exdata_plotting1

Plotting Assignment 1 for Exploratory Data Analysis

grf

Generalized Random Forests

lifelines

Survival analysis in Python

lifetimes

Lifetime value in Python

probabilistic-programming-and-bayesian-methods-for-hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

programmingassignment2

Repository for Programming Assignment 2 for R Programming on Coursera

pymc3

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara

repad

Recursive Partitioning Discretization for dynamic discrete choice modeling

repdata_peerassessment1

Peer Assessment 1 for Reproducible Research

robyn

Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) package from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define media channel efficiency and effectivity, explore adstock rates and saturation curves. It's built for granular datasets with many independent variables and therefore especially suitable for digital and direct response advertisers with rich dataset.

rtweet

🐦 R client for interacting with Twitter's [stream and REST] APIs

stylegan

StyleGAN - Official TensorFlow Implementation

tidydata

Assignment for Getting and Cleaning Data Course on Coursera

tweepy

Twitter for Python!

yolov5

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite