Metadata-Version: 2.1
Name: apriorib1
Version: 1.0.2
Summary: A Python library that implements smooth and easy apriori for association rule mining. Currently limited for maximum 4 items/transaction.
Home-page: https://github.com/BALaka-18/apriorib1
Author: Balaka Biswas
Author-email: balaka2605@gmail.com
License: MIT
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](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/)
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
