https://github.com/dr-saad-la/dxminer

Data Extensible miner python library
https://github.com/dr-saad-la/dxminer

Last synced: 7 days ago
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Data Extensible miner python library

• Host: GitHub
• URL: https://github.com/dr-saad-la/dxminer
• Owner: dr-saad-la
• License: mit
• Created: 2024-08-31T00:04:29.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-09-16T22:39:40.000Z (2 months ago)
• Last Synced: 2024-09-17T16:26:35.876Z (about 2 months ago)
• Language: Python
• Size: 101 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • Changelog: HISTORY.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.rst
  • Authors: AUTHORS.rst

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README

        
=======

DXMiner

=======

.. image:: https://img.shields.io/pypi/v/dxminer.svg

        :target: https://pypi.python.org/pypi/dxminer

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     :alt: Updates

DXMiner is a high-level Python package designed for comprehensive data-intensive tasks. It streamlines the processes of data mining, exploration, and visualization, and facilitates efficient model building. DXMiner is ideal for users who need a robust toolkit to handle extensive datasets and complex analytical workflows.

* Free software: MIT license

* Documentation: https://dxminer.readthedocs.io.

Introduction

------------

DXMiner simplifies the workflow for data scientists, analysts, and researchers who deal with large datasets. It offers a unified interface for data preprocessing, exploration, visualization, and machine learning model development.

Installation

------------

To install DXMiner, simply run:

.. code-block:: bash

    pip install dxminer

Getting Started

---------------

Here’s a quick example to get you started with DXMiner:

.. code-block:: python

    import dxminer as dx

    # Load a dataset

    data = dx.load_dataset('sample_data.csv')

    # Perform data exploration

    dx.explore(data)

Features

--------

DXMiner provides a range of features, including:

- **Data Loading and Preprocessing**: Seamlessly load and clean datasets.

- **Exploration and Visualization**: Explore datasets with powerful visualization tools.

- **Model Building and Evaluation**: Build, train, and evaluate machine learning models.

- **Integration**: Works well with other popular data science libraries like Pandas, NumPy, and Scikit-learn.

Documentation

-------------

Full documentation is available at the following link:

`DXMiner Documentation `_

Contributing

------------

We welcome contributions! If you’d like to contribute to DXMiner, please check out our contributing guidelines:

- Report bugs and request features via the issue tracker.

- Submit pull requests with new features or bug fixes.

- For more details, see our CONTRIBUTING.md file.

License

-------

This project is licensed under the MIT License - see the LICENSE file for details.

Support and Contact

-------------------

For support, please open an issue in our issue tracker. You can also reach out to the maintainers via email.

Changelog

---------

For a detailed list of changes, please refer to the `Changelog `_.

Examples

--------

Explore our `Examples `_ section for detailed use cases and tutorials on how to use DXMiner for various data tasks.