Smart Business Processes

Use data science to automate and optimize business processes

Example applications of data science to business processes include using customer behavioral data to offer them a better experience, such as a discount when they are about to leave a site. Besides increasing revenue, data science can help decrease internal costs, for example by accurately predicting the stock required.

Thomas Ebermann
Lead Data Services
Emilie Boillat
Business Developer
Sabine Maennel
Software Engineer

Prototypes and Proof of Concepts

Experimental applications that we build to prove feasibility or to test user acceptance.

Inspiration

Collection of innovative ideas and best practices which inspire us.

Tools and Frameworks

A curated collection of tools and frameworks relevant for Smart Business Processes systems. More under http://datasciencestack.liip.ch
Lore
Maintainable ML approachable for Software Engineers.
Tuber
Youtube Analytics in R
Anodot
Anomaly detection
Papermill
Parametrize jupyter notebooks
Scikit Learn
A Python module for machine learning built on top of SciPy.
Dlib
Like Scikit Learn but for C
DMTK
Distributed Machine Learning Toolkit from Microsoft
Sklearn-Pandas
Scikit-Learn for pandas data frames
Dask
Parallel Computing for Python
Dask-learn
Scikit-Learn parallel computing
PhpML
Machine Learning in PHP
REP
Ipython Scikit Extension
XBoost
Python bindings for eXtreme Gradient Boosting (Tree) Librar
Nteract
Best of jupyter notebooks
Scrapbook
Save Jupyter notebook outputs
Scipy
Python framework for mathematics, science, and engineering.
Numpy
A fundamental package for scientific computing with Python.
Statsmodels
Statistical modeling and econometrics in Python.
AutoML
Automated machine learning pipelines for analytics and production
Hyperopt
Automatic Hyperparameter optimization
Algorithmia
ML Algorithms as a marketplace
Pandas Profiling
Have a first look at your data in seconds
Aerosolve
A human friendly machine learning library by Airbnb.
NetworkX
A high-productivity software for complex networks
Prophet
Time Series forecasting from Facebook
Unplugg
Time Series Prediction
Shogun
Machine Learning for C++
PyMc3
Machine Learning Bayesian Modeling
automl-gs
CSV to ML
xgboost
Fast optimized ML
Catboost
Open Source gradient boosting machine from Yandex
Lightgbm
Light gradient boosting machine from Microsoft
JS-Recommender
Recommender in JS
TPOT
AutoML for Python
Featuretools
Automated Feature Generation for Pandas
Pandas-Overview
An extension to pandas dataframes describe function
Pandas-Profiling
Create HTML profiling reports from pandas DataFrame objects
LIME
Explaining the predictions of any machine learning classifier
SHAP
A unified approach to explain the output of any machine learning model
Automl-gs
Easy AutoML
eli5
ML Explainer
Lightning
Large-scale linear classification
py-earth
Piecewise linear ML
imbalanced-learn
Imbalanced Learn techniques
Nbdime
Git Merge Jupyter notebooks