All Projects → sayakpaul → A-B-testing-with-Machine-Learning

sayakpaul / A-B-testing-with-Machine-Learning

Licence: other
Implemented an A/B Testing solution with the help of machine learning

Programming Languages

Jupyter Notebook
11667 projects

Projects that are alternatives of or similar to A-B-testing-with-Machine-Learning

Mars
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
Stars: ✭ 2,308 (+6137.84%)
Mutual labels:  xgboost, statsmodels
kserve
Serverless Inferencing on Kubernetes
Stars: ✭ 1,621 (+4281.08%)
Mutual labels:  sklearn, xgboost
M2cgen
Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
Stars: ✭ 1,962 (+5202.7%)
Mutual labels:  xgboost, statsmodels
ml-time-series-analysis-on-sales-data
Time Series Decomposition techniques and random forest algorithm on sales data
Stars: ✭ 34 (-8.11%)
Mutual labels:  sklearn, statsmodels
Hyperparameter hunter
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
Stars: ✭ 648 (+1651.35%)
Mutual labels:  sklearn, xgboost
Open Solution Value Prediction
Open solution to the Santander Value Prediction Challenge 🐠
Stars: ✭ 34 (-8.11%)
Mutual labels:  sklearn, xgboost
Automl alex
State-of-the art Automated Machine Learning python library for Tabular Data
Stars: ✭ 132 (+256.76%)
Mutual labels:  sklearn, xgboost
HumanOrRobot
a solution for competition of kaggle `Human or Robot`
Stars: ✭ 16 (-56.76%)
Mutual labels:  sklearn, xgboost
Ai competitions
AI比赛相关信息汇总
Stars: ✭ 443 (+1097.3%)
Mutual labels:  sklearn, xgboost
Kfserving
Serverless Inferencing on Kubernetes
Stars: ✭ 809 (+2086.49%)
Mutual labels:  sklearn, xgboost
Tensorflow Ml Nlp
텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)
Stars: ✭ 176 (+375.68%)
Mutual labels:  sklearn, xgboost
Auto ts
Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.
Stars: ✭ 195 (+427.03%)
Mutual labels:  sklearn
Ml Cheatsheet
A constantly updated python machine learning cheatsheet
Stars: ✭ 136 (+267.57%)
Mutual labels:  sklearn
Qlik Py Tools
Data Science algorithms for Qlik implemented as a Python Server Side Extension (SSE).
Stars: ✭ 135 (+264.86%)
Mutual labels:  sklearn
Machine Learning Projects
This repository consists of all my Machine Learning Projects.
Stars: ✭ 135 (+264.86%)
Mutual labels:  sklearn
Mozart
An optical music recognition (OMR) system. Converts sheet music to a machine-readable version.
Stars: ✭ 241 (+551.35%)
Mutual labels:  sklearn
Data Science Notebook
📖 每一个伟大的思想和行动都有一个微不足道的开始
Stars: ✭ 196 (+429.73%)
Mutual labels:  sklearn
Role2vec
A scalable Gensim implementation of "Learning Role-based Graph Embeddings" (IJCAI 2018).
Stars: ✭ 134 (+262.16%)
Mutual labels:  sklearn
Ds Ai Tech Notes
📖 [译] 数据科学和人工智能技术笔记
Stars: ✭ 131 (+254.05%)
Mutual labels:  sklearn
docker-kaggle-ko
머신러닝/딥러닝(PyTorch, TensorFlow) 전용 도커입니다. 한글 폰트, 한글 자연어처리 패키지(konlpy), 형태소 분석기, Timezone 등의 설정 등을 추가 하였습니다.
Stars: ✭ 46 (+24.32%)
Mutual labels:  xgboost

Recently, I was reading through A/B Testing with Machine Learning - A Step-by-Step Tutorial written by Matt Dancho of Business Science. I have been always fascinated by the idea of A/B Testing and the amount of impact it can bring in businesses. The tutorial is very definitive and Matt has explained each and every step in the tutorial. He has detailed about each and every decision taken while developing the solution.

Even though the tutorial is written in R, I was able to scram through his code and my knowledge of Data Science helped me to understand the concepts very quickly. I will have to thank Matt for putting together all the key ingredients of the Data Science world and or using them to solve a real problem.

The notebook in this repository contains my implementation of the solution (presented by Matt) in Python.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].