https://github.com/balaka-18/apriorib1

apriorib1 is a Python library that applies the very famous unsupervised learning algorithm, apriori, for Association Rule Mining(ARM) on a dataset of transaction/purchase logs and shows the accepted association rules.
https://github.com/balaka-18/apriorib1

apriori association-rule-mining association-rules grocery-store unsupervised-learning

Last synced: 4 months ago
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apriorib1 is a Python library that applies the very famous unsupervised learning algorithm, apriori, for Association Rule Mining(ARM) on a dataset of transaction/purchase logs and shows the accepted association rules.

• Host: GitHub
• URL: https://github.com/balaka-18/apriorib1
• Owner: BALaka-18
• License: mit
• Created: 2020-08-19T12:44:18.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-08-19T12:54:52.000Z (about 5 years ago)
• Last Synced: 2025-06-06T23:08:46.642Z (5 months ago)
• Topics: apriori, association-rule-mining, association-rules, grocery-store, unsupervised-learning
• Language: Python
• Homepage:
• Size: 17.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

          
# apriorib1

apriorib1 is a Python library that applies the very famous unsupervised learning algorithm, apriori, for Association Rule Mining(ARM) on a dataset of transaction/purchase logs and shows the accepted association rules. 

Currently, this version is limited to a maximum of 4 items in a certain transaction.

![Demo](https://user-images.githubusercontent.com/49288068/90564473-10814100-e1c3-11ea-81c4-4a5a37abff75.png)

## New in this version

1. Displays stage-wise final itemset as pandas DataFrames.

![Demo 2](https://user-images.githubusercontent.com/49288068/90564809-8d141f80-e1c3-11ea-8e2c-d81c08567643.png)

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install apriorib1.

```bash

pip install apriorib1

```

## Quick Start

```python

from apriorib1 import Apriori

data = [['MILK', 'BREAD', 'BISCUIT'],

    ['BREAD', 'MILK', 'BISCUIT', 'CORNFLAKES'],

    ['BREAD', 'TEA', 'BOURNVITA'],

    ['JAM', 'MAGGI', 'BREAD', 'MILK'],

    ['MAGGI', 'TEA', 'BISCUIT'],

    ['BREAD', 'TEA', 'BOURNVITA'],

    ['MAGGI', 'TEA', 'CORNFLAKES'],

    ['MAGGI', 'BREAD', 'TEA', 'BISCUIT'],

    ['JAM', 'MAGGI', 'BREAD', 'TEA'],

    ['BREAD', 'MILK'],

    ['COFFEE', 'COCK', 'BISCUIT', 'CORNFLAKES'],

    ['COFFEE', 'COCK', 'BISCUIT', 'CORNFLAKES'],

    ['COFFEE', 'SUGER', 'BOURNVITA'],

    ['BREAD', 'COFFEE', 'COCK'],

    ['BREAD', 'SUGER', 'BISCUIT'],

    ['COFFEE', 'SUGER', 'CORNFLAKES'],

    ['BREAD', 'SUGER', 'BOURNVITA'],

    ['BREAD', 'COFFEE', 'SUGER'],

    ['BREAD', 'COFFEE', 'SUGER'],

    ['TEA', 'MILK', 'COFFEE', 'CORNFLAKES']]

# Testing the Apriori class

apr = Apriori(records=data,min_sup=2,min_conf=50)

df1,df2,df3,df4 = apr.show_as_df(stage=1),apr.show_as_df(stage=2),apr.show_as_df(stage=3),apr.show_as_df(stage=4)

print("VIEWING THE ITEMSET DATAFRAMES AT THE DIFFERENT STAGES :\nSTAGE 1\n{}\nSTAGE 2\n{}\nSTAGE 3\n{}\nSTAGE 4\n{}".format(df1,df2,df3,df4))

apr.checkAssc()

```

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License

[MIT](https://choosealicense.com/licenses/mit/)