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Package with many useful helpers for making data science being even more enjoyable. It provides utility functions, computational routines, visualizations, etc. for exploratory analysis, classification, regression, etc. along with examples in jupyter notebooks.

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README.md

Data Science Helpers

This package provides a variety of useful functions for data science for exploratory analysis, correlation analysis, classification, regression, etc. It contains a handful of useful utility functions as well as modern-looking and easy-to-use visualizations that make playing with the data a real joy.

The main package that contains all the magic is named helpers. For more information, please see the description provided in the docstrings or take a look at the jupyter notebooks for each of the packages.

This package is developed by the members of Brain Disease Analysis Laboratory (BDALab). For more information, please contact the main author: Zoltan Galaz at zoltan@galaz.eu.


Installation

git clone git@github.com:zgalaz/data-science-helpers.git
cd data-science-helpers
python3 -m virtualenv .venv
source .venv/bin/activate
pip install .

Structure

+---helpers
|   +---classification
|   |   |   metrics.py
|   |   |   validation.py
|   |   |   visualization.py
|   |           
|   +---common
|   |   |   visualization.py
|   |           
|   +---correlation
|   |   |   computation.py
|   |   |   visualization.py
|   |           
|   +---exploration
|   |   |   visualization.py
|   |           
|   +---regression
|   |   |   metrics.py
|   |   |   validation.py
|   |           
|   \---utils
|       |   logger.py
|       |   transformers.py
|       |   validators.py
|               
+---notebooks
    |   classification_notebook.ipynb
    |   correlation_notebook.ipynb
    |   exploration_notebook.ipynb
    |   regression_notebook.ipynb
    |   utils_notebook.ipynb

License

This project is licensed under the terms of the MIT license. For more details, see the LICENSE file.

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Package with many useful helpers for making data science being even more enjoyable. It provides utility functions, computational routines, visualizations, etc. for exploratory analysis, classification, regression, etc. along with examples in jupyter notebooks.

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