https://github.com/asaini/Apriori

Python Implementation of Apriori Algorithm for finding Frequent sets and Association Rules
https://github.com/asaini/Apriori

Last synced: about 2 months ago
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Python Implementation of Apriori Algorithm for finding Frequent sets and Association Rules

• Host: GitHub
• URL: https://github.com/asaini/Apriori
• Owner: asaini
• License: mit
• Created: 2011-12-05T04:43:06.000Z (almost 13 years ago)
• Default Branch: python3
• Last Pushed: 2022-09-10T16:01:18.000Z (about 2 years ago)
• Last Synced: 2024-06-27T19:53:18.417Z (3 months ago)
• Language: Python
• Homepage:
• Size: 6.46 MB
• Stars: 764
• Watchers: 40
• Forks: 434
• Open Issues: 14
• Metadata Files:
  • Readme: README.md
  • License: mit-license

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README

        
Python Implementation of Apriori Algorithm 

==========================================

## Set up

[![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io/asaini/apriori/python3) [![Build Status](https://travis-ci.org/asaini/Apriori.svg?branch=master)](https://travis-ci.org/asaini/Apriori) 

Edit without local environment setup

[![Open in Gitpod](https://gitpod.io/button/open-in-gitpod.svg)](https://gitpod.io/#https://github.com/asaini/Apriori)

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## Acknowledgements

The code attempts to implement the following paper:

> *Agrawal, Rakesh, and Ramakrishnan Srikant. "Fast algorithms for mining association rules." Proc. 20th int. conf. very large data bases, VLDB. Vol. 1215. 1994.*

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Interactive Streamlit App

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To view a live interactive app, and play with the input values, please click [here](https://share.streamlit.io/asaini/apriori/python3). This app was built using [Streamlit](https://www.streamlit.io) 😎, the source code for the app can be found [here](https://github.com/asaini/Apriori/blob/python3/streamlit_app.py)

Running the Streamlit app locally

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To run the interactive Streamlit app with dataset  

    $ pip3 install -r requirements.txt

    $ streamlit run streamlit_app.py

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CLI Usage

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To run the program with dataset provided and default values for *minSupport* = 0.15 and *minConfidence* = 0.6

    python apriori.py -f INTEGRATED-DATASET.csv

To run program with dataset  

    python apriori.py -f INTEGRATED-DATASET.csv -s 0.17 -c 0.68

Best results are obtained for the following values of support and confidence:  

Support     : Between 0.1 and 0.2  

Confidence  : Between 0.5 and 0.7 

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Datasets

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#### INTEGRATED-DATASET.csv

The dataset is a copy of the “Online directory of certified businesses with a detailed profile” file from the Small Business Services (SBS) 

dataset in the `NYC Open Data Sets `_

#### tesco.csv

Toy dataset of items from shopping cart

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License

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MIT-License