=============================== SciKit Data

.. image:: https://img.shields.io/pypi/v/scikit-data.svg :target: https://pypi.python.org/pypi/scikit-data

.. image:: https://img.shields.io/travis/OpenDataScienceLab/skdata.svg :target: https://travis-ci.org/OpenDataScienceLab/skdata

.. image:: https://readthedocs.org/projects/skdata/badge/?version=latest :target: https://skdata.readthedocs.io/en/latest/?badge=latest :alt: Documentation Status

Conda package current release info

.. image:: https://anaconda.org/conda-forge/scikit-data/badges/version.svg :target: https://anaconda.org/conda-forge/scikit-data :alt: Anaconda-Server Badge

.. image:: https://anaconda.org/conda-forge/scikit-data/badges/downloads.svg :target: https://anaconda.org/conda-forge/scikit-data :alt: Anaconda-Server Badge

About SciKit Data

The propose of this library is to allow the data analysis process more easy and automatic.

General objectives:

• reduce boilerplate code;
• reduce time spent on data analysis tasks and;
• offer a reproducible data analysis workflow.

Generally, there is a lot of boilerplate code on data analysis task that could be resolved with reproducible mechanisms and easy data visualization methods. Another point is related to data publish. A lot of data analysts doesn't know about open data repositories or doesn't consider that in his/her scientific workflow communication.

Specifics objectives:

• optimize data visualization;
• integration with open data repositories to publish data;
• reproducibility on data analysis tasks through storing and recovery operations;

SkData should integrate with Pandas library (Python).

Books used as reference to guide this project:

• https://www.packtpub.com/big-data-and-business-intelligence/clean-data
• https://www.packtpub.com/big-data-and-business-intelligence/python-data-analysis
• https://www.packtpub.com/big-data-and-business-intelligence/mastering-machine-learning-scikit-learn
• https://www.packtpub.com/big-data-and-business-intelligence/practical-data-analysis-second-edition

Some other materials used as reference:

• https://github.com/rsouza/MMD/blob/master/notebooks/3.1_Kaggle_Titanic.ipynb
• https://github.com/agconti/kaggle-titanic/blob/master/Titanic.ipynb
• https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb

Installing scikit-data

Using conda

Installing scikit-data from the conda-forge channel can be achieved by adding conda-forge to your channels with:

.. code-block:: console

$ conda config --add channels conda-forge

Once the conda-forge channel has been enabled, scikit-data can be installed with:

.. code-block:: console

$ conda install scikit-data

It is possible to list all of the versions of scikit-data available on your platform with:

.. code-block:: console

$ conda search scikit-data --channel conda-forge

Using pip

To install scikit-data, run this command in your terminal:

.. code-block:: console

$ pip install skdata

If you don't have pip_ installed, this Python installation guide_ can guide you through the process.

.. _pip: https://pip.pypa.io .. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/

More Information

• License: MIT
• Documentation: https://skdata.readthedocs.io

References

• CUESTA, Hector; KUMAR, Sampath. Practical Data Analysis. Packt Publishing Ltd, 2016.

Electronic materials

• [1] http://www.datasciencecentral.com/profiles/blogs/introduction-to-outlier-detection-methods