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A Python library that implements smooth and easy apriori for association rule mining. Currently limited for maximum 4 items/transaction.

Project description

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

New in this version

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

Demo 2

Installation

Use the package manager pip to install apriorib1.

pip install apriorib1

Quick Start

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

Project details


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