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all-programming-e-books-pdf icon all-programming-e-books-pdf

A Curated List Of Programming Books For C, C++ , Python, JavaScript, NodeJs, ReactJs, Web, JQuery, Flask, Dom, Angular, CSS, HTML for beginners, intermediate, advanced and experts

api-samples icon api-samples

Code samples for YouTube APIs, including the YouTube Data API, YouTube Analytics API, and YouTube Live Streaming API. The repo contains language-specific directories that contain the samples.

causalml icon causalml

Uplift modeling and causal inference with machine learning algorithms

data-science-hacks icon data-science-hacks

Data Science Hacks consists of tips, tricks to help you become a better data scientist. Data science hacks are for all - beginner to advanced. Data science hacks consist of python, jupyter notebook, pandas hacks and so on.

dataeditr icon dataeditr

An Interactive R Package for Viewing, Entering Filtering and Editing Data

free_r_tips icon free_r_tips

Free R-Tips is a FREE Newsletter provided by Business Science. It comes with bite-sized code tutorials every Tuesday.

gargle icon gargle

Infrastructure for calling Google APIs from R, including auth

hello-world icon hello-world

The repository is about writing "Hello World" @ different programming languages

lightweight_mmm icon lightweight_mmm

LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.

mlforecast icon mlforecast

Scalable machine learning based time series forecasting

nns icon nns

Nonlinear Nonparametric Statistics

pyreadr icon pyreadr

Python package to read and write R RData and Rds files into/from pandas dataframes. No R or other external dependencies required.

python-youtube-api icon python-youtube-api

A basic Python YouTube v3 API to fetch data from YouTube using public API-Key without OAuth

robyn icon robyn

Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) code from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression with cross validation, 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.

rr-r-publication icon rr-r-publication

Reproducible Research Workshop Demo repository for publication writing in R

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