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:dart: Personal data science and machine learning toolbox
| Date | Stars |
|---|---|
| 2026-07-31 | 364 |
| 2026-08-05 | 364 |
| 2026-08-06 | 364 |
Today
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growth rate 0.00%/day
# xam [](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)
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:895e09fbdeaf09b7, llm:Repository description and README: 'personal data science and machine learning toolbox', topics: data-science, machine-learning, preprocessing, stacking. README lists ensembling, EDA, feature extraction, selection, model selection, linear models — a Python toolbox for ML workflows.
matched fp:895e09fbdeaf09b7, llm:Repository description and README: 'personal data science and machine learning toolbox', topics: data-science, machine-learning, preprocessing, stacking. README lists ensembling, EDA, feature extraction, selection, model selection, linear models — a Python toolbox for ML workflows.