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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 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.


New in this version

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

Demo 2


Use the package manager pip to install apriorib1.

pip install apriorib1

Quick Start

from apriorib1 import Apriori

data = [['MILK', 'BREAD', 'BISCUIT'],
    ['BREAD', 'TEA', 'BOURNVITA'],
    ['JAM', 'MAGGI', 'BREAD', 'MILK'],
    ['MAGGI', 'TEA', 'BISCUIT'],
    ['BREAD', 'TEA', 'BOURNVITA'],
    ['MAGGI', 'BREAD', 'TEA', 'BISCUIT'],
    ['JAM', 'MAGGI', 'BREAD', 'TEA'],
    ['BREAD', 'MILK'],
    ['BREAD', 'COFFEE', 'COCK'],
    ['BREAD', 'SUGER', 'BISCUIT'],
    ['BREAD', 'COFFEE', 'SUGER'],
    ['BREAD', 'COFFEE', 'SUGER'],

# 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))


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.



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