netbg 0.1

A simple package for Big Data Analytics Practical

NetBG Package

NetBG is a Python package designed for various data processing and analysis tasks, providing a suite of methods for handling different algorithms and techniques in network analysis, data mining, and more.

Installation

To install the NetBG package, you can use pip:

pip install netbg

Usage

To use the NetBG package, you can import it as follows:

import netbg as ng

Available Methods

The following methods are available in the NetBG package:

1. crud() - Perform Create, Read, Update, and Delete operations.
2. logistic() - Implement logistic regression for classification tasks.
3. pipeline() - Create a processing pipeline for data transformations.
4. shingles_word() or shingles_char() - Generate word or character shingles from text.
5. minhash() - Perform MinHash for estimating the similarity between datasets.
6. minhashpro() - Apply MinHash for k-shingles.
7. martin() - Implement the Martin algorithm for network analysis.
8. bloom() - Use Bloom filters for probabilistic data structures.
9. ams() - Apply Alon-Matias-Szegedy algorithm for frequency estimation.
10. bipartite() - Analyze bipartite graphs.
11. social() - Implement social network analysis methods.
12. pcy() - Use the PCY algorithm for frequent itemset mining.

Example

Here's an example of how to use the social method:

import netbg as ng

# Call the social method
ng.social()

Documentation

For more detailed information on each method, please refer to the official documentation or the source code within the package.

License

This package is licensed under the MIT License. See the LICENSE file for more information.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or suggestions.