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https://github.com/MaxHalford/xam

:dart: Personal data science and machine learning toolbox
https://github.com/MaxHalford/xam

data-science machine-learning preprocessing python stacking

Last synced: 3 months ago
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:dart: Personal data science and machine learning toolbox

Host: GitHub
URL: https://github.com/MaxHalford/xam
Owner: MaxHalford
License: mit
Created: 2017-01-24T20:24:58.000Z (almost 8 years ago)
Default Branch: master
Last Pushed: 2020-02-04T20:37:10.000Z (almost 5 years ago)
Last Synced: 2024-05-01T15:27:50.654Z (6 months ago)
Topics: data-science, machine-learning, preprocessing, python, stacking
Language: Python
Homepage:
Size: 1.12 MB
Stars: 361
Watchers: 21
Forks: 78
Open Issues: 3
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE

Awesome Lists containing this project

README

        # xam [![Build Status](https://travis-ci.org/MaxHalford/xam.svg?branch=master)](https://travis-ci.org/MaxHalford/xam)

`xam` is my personal data science and machine learning toolbox. It is written in Python 3 and stands on the shoulders of giants (mainly [pandas](https://pandas.pydata.org/) and [scikit-learn](http://scikit-learn.org/)). It loosely follows scikit-learn's `fit`/`transform`/`predict` convention.

## Installation

- [Install Anaconda for Python 3.x >= 3.5](https://www.continuum.io/downloads)

- Run `pip install git+https://github.com/MaxHalford/xam --upgrade` in a terminal

:warning: Because xam is a ***personal*** toolkit, the `--upgrade` flag will install the latest releases of each dependency (scipy, pandas etc.). I like to stay up-to-date with the latest library versions.

## Table of contents

Usage example is available in the [docs](docs) folder. Each example is tested with [doctest](https://pymotw.com/2/doctest/).

- [Ensembling](docs/ensembling.md)

  - [Groupby model](docs/ensembling.md#groupby-model)

  - [LightGBM with CV](docs/ensembling.md#lightgbm-with-cv)

  - [Stacking](docs/ensembling.md#stacking)

  - [Stacking with bagged test predictions](docs/ensembling.md#stacking-with-bagged-test-predictions)

- [Exploratory data analysis (EDA)](docs/eda.md)

  - [Feature importance](docs/eda.md#feature-importance)

- [Feature extraction](docs/feature-extraction.md)

  - [Bayesian target encoding](docs/feature-extraction.md#bayesian-target-encoding)

  - [Combining features](docs/feature-extraction.md#combining-features)

  - [Count encoding](docs/feature-extraction.md#count-encoding)

  - [Cyclic features](docs/feature-extraction.md#cyclic-features)

- [Feature selection](docs/feature-selection.md)

  - [Forward-backward selection](docs/feature-selection#forward-backward-selection)

- [Linear models](docs/linear-models.md)

  - [AUC regressor](docs/linear-models.md#auc-regressor)

- [Model selection](docs/model-selection.md)

  - [Ordered cross-validation](docs/model-selection.md#ordered-cross-validation)

- [Natural Language Processing (NLP)](docs/nlp.md)

  - [NB-SVM](docs/nlp.md#nb-svm)

  - [Norvig spelling corrector](docs/nlp.md#norvig-spelling-corrector)

  - [Top-terms classifier](docs/nlp.md#top-terms-classifier)

- [Pipeline](docs/pipeline.md)

  - [Column selection](docs/pipeline.md#column-selection)

  - [Series transformer](docs/pipeline.md#series-transformer)

  - [DataFrame transformer](docs/pipeline.md#dataframe-transformer)

  - [Lambda transformer](docs/pipeline.md#lambda-transformer)

- [Plotting](docs/plotting.md)

  - [Latex style figures](docs/plotting.md#latex-style-figures)

- [Preprocessing](docs/preprocessing.md)

  - [Binning](docs/preprocessing.md#binning)

  - [Groupby transformer](docs/preprocessing.md#groupby-transformer)

  - [One-hot encoding](docs/preprocessing.md#one-hot-encoding)

  - [Resampling](docs/preprocessing.md#resampling)

- [Time series analysis (TSA)](docs/tsa.md)

  - [Exponentially weighted average](docs/tsa.md#ewm-optimization)

  - [Exponential smoothing](docs/tsa.md#exponential-smoothing)

  - [Frequency average forecasting](docs/tsa.md#frequency-average-forecasting)

- [Various](docs/various.md)

  - [Datetime range](docs/various.md#datetime-range)

  - [Next day of the week](docs/various.md#next-day-of-the-week)

  - [Subsequence lengths](docs/various.md#subsequence-lengths)

  - [DataFrame to Vowpal Wabbit](docs/various.md#dataframe-to-vowpal-wabbit)

  - [Normalized compression distance](docs/various.md#normalized-compression-distance)

  - [Skyline querying](docs/various.md#skyline-querying)

  - [Fuzzy duplicates](docs/various.md#fuzzy-duplicates)

## Other Python data science and machine learning toolkits

- [fastai/fastai](https://github.com/fastai/fastai)

- [Laurae2/Laurae](https://github.com/Laurae2/Laurae)

- [rasbt/mlxtend](https://github.com/rasbt/mlxtend)

- [reiinakano/scikit-plot](https://github.com/reiinakano/scikit-plot)

- [scikit-learn-contrib](https://github.com/scikit-learn-contrib)

- [zygmuntz/phraug2](https://github.com/zygmuntz/phraug2)

## License

The MIT License (MIT). Please see the [license file](LICENSE) for more information.