mlmachine 0.0.3

Accelerate machine learning experimentation

# mlmachine

mlmachine accelerates the end-to-end machine learning pipeline.

This library is for those who have…

…copied a well-worn block of code and painstakingly adapted it to a new problem. …wished it wasn’t so tedious to perform exploratory data analysis.

mlmachine stands in the shoulders of these great packages:

[catboost](https://github.com/catboost/catboost) | [eif](https://github.com/sahandha/eif) | [hyperopt](https://github.com/hyperopt/hyperopt) | [imbalanced-learn](https://github.com/scikit-learn-contrib/imbalanced-learn) | [jupyter](https://github.com/jupyter/notebook) | [lightgbm](https://github.com/microsoft/LightGBM) | [matplotlib](https://github.com/matplotlib/matplotlib) | [numpy](https://github.com/numpy/numpy) | [pandas](https://github.com/pandas-dev/pandas) | [prettierplot](https://github.com/petersontylerd/prettierplot) | [scikit-learn](https://github.com/scikit-learn/scikit-learn) | [scipy](https://github.com/scipy/scipy) | [seaborn](https://github.com/mwaskom/seaborn) | [shap](https://github.com/slundberg/shap) | [statsmodels](https://github.com/statsmodels/statsmodels) | [xgboost](https://github.com/dmlc/xgboost) |