SciKit Data

Conda package current release info

About SciKit Data

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

This library is based on some important libraries as:

• pandas;
• jupyter;
• matplotlib;
• scikit-learn;

• Free software: MIT license
• Documentation: https://skdata.readthedocs.io.

Features

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

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

This project contemplates the follow features:

• Data conversions:
  • soon ...
• Data collection:
  • soon ...
• Data cleaning:
  • ...
• Data storage:
  • soon ...
• Data integration:
  • soon ...
• Data manipulation:
  • ...
• Outliers removal:
  • ...

Installing scikit-data

Using conda

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

$ conda config --add channels conda-forge

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

$ conda install scikit-data

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

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

Using pip

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

$ pip install skdata

If you don’t have pip installed, this Python installation guide can guide you through the process.