nublu1234's Stars
gsbDBI/ExperimentData
InstaPy/instagram-profilecrawl
π quickly crawl the information (e.g. followers, tags etc...) of an instagram profile.
Datalux/Osintgram
Osintgram is a OSINT tool on Instagram. It offers an interactive shell to perform analysis on Instagram account of any users by its nickname
dvingerh/PyInstaLive
Python script to download Instagram livestreams.
maibennett/sta235
STA 235 - Data Science for Business Applications
maks-sh/scikit-uplift
:exclamation: uplift modeling in scikit-learn style in python :snake:
johaupt/johaupt.github.io
bookingcom/upliftml
UpliftML: A Python Package for Scalable Uplift Modeling
bashtage/linearmodels
Additional linear models including instrumental variable and panel data models that are missing from statsmodels.
madina-k/DSE2021_tutorials
AliciaCurth/CATENets
Sklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators.
projectmesa/mesa
Mesa is an open-source Python library for agent-based modeling, ideal for simulating complex systems and exploring emergent behaviors.
matteocourthoud/Blog-Posts
Code and notebooks for my Medium blog posts
amber-kshz/PRML
Python implementations (on jupyter notebook) of algorithms described in the book "PRML"
d2cml-ai/14.388_py
This material has been created based on the tutorials of the course 14.388 Inference on Causal and Structural Parameters Using ML and AI in the Department of Economics at MIT taught by Professor Victor Chernozukhov. All the scripts were in R and we decided to translate them into Python, so students can manage both programing languages. Jannis Kueck and V. Chernozukhov have also published the original R Codes in Kaggle. In adition, we included tutorials on Heterogenous Treatment Effects Using Causal Trees and Causal Forest from Susan Atheyβs Machine Learning and Causal Inference course. We aim to add more empirical examples were the ML and CI tools can be applied using both programming languages.
d2cml-ai/mgtecon634_py
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.
spencerbraun/stats_notes
Markdown and LaTeX Notes from Stats MS
zalando/expan
Open-source Python library for statistical analysis of randomised control trials (A/B tests)
educauchy/auto-ab
An approach of the development an automated A/B experiments platform
brian-pantano/PianoFromAbove
ZaneH/piano-trainer
Memorize piano scales with ease! A piano practice program w/ MIDI support. Consider it an interactive reference manual πΉ
kosua20/MIDIVisualizer
A small MIDI visualizer tool, using OpenGL
playinpap/awesome-data-and-analytics-governance
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diligejy/Growth
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Gru80/obsidian-regex-replace
Plugin for Obsidian, providing search/replace functionality which supports regular expressions and selections.
luelhagos/abtest-mlops
A/B testing with Machine Learning
pyglet/pyglet
pyglet is a cross-platform windowing and multimedia library for Python, for developing games and other visually rich applications.
probml/pml2-book
Probabilistic Machine Learning: Advanced Topics
ml-tooling/best-of-ml-python
π A ranked list of awesome machine learning Python libraries. Updated weekly.
ckbjimmy/2019_tokyo
2019 causal inference workshop