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align=center\u003e\n    \u003cimg src=\"apriori.PNG\" width=\"440\" height=\"331\"\u003e\n\u003c/p\u003e\n\n\u003cp align=center\u003e\n    \u003ca target=\"_blank\" href=\"https://pypi.org/project/apriori-python/\" title=\"pip\"\u003e\u003cimg src=\"https://img.shields.io/pypi/v/apriori_python?color=brightgreen\"\u003e\u003c/a\u003e\n    \u003ca target=\"_blank\" href=\"#\" title=\"language count\"\u003e\u003cimg src=\"https://img.shields.io/github/languages/count/chonyy/apriori_python\"\u003e\u003c/a\u003e\n    \u003ca target=\"_blank\" href=\"#\" title=\"top language\"\u003e\u003cimg src=\"https://img.shields.io/github/languages/top/chonyy/apriori_python?color=orange\"\u003e\u003c/a\u003e\n    \u003ca target=\"_blank\" href=\"https://opensource.org/licenses/MIT\" title=\"License: MIT\"\u003e\u003cimg src=\"https://img.shields.io/badge/License-MIT-blue.svg\"\u003e\u003c/a\u003e\n    \u003ca target=\"_blank\" href=\"#\" title=\"repo size\"\u003e\u003cimg src=\"https://img.shields.io/github/repo-size/chonyy/apriori_python\"\u003e\u003c/a\u003e\n    \u003ca target=\"_blank\" href=\"http://makeapullrequest.com\" title=\"PRs Welcome\"\u003e\u003cimg src=\"https://img.shields.io/badge/PRs-welcome-brightgreen.svg\"\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n## Getting Started\n\n### Install the Pypi package using pip\n\n```\npip install apriori_python\n```\n\nThen use it like \n\n```python\nfrom apriori_python import apriori\nitemSetList = [['eggs', 'bacon', 'soup'],\n                ['eggs', 'bacon', 'apple'],\n                ['soup', 'bacon', 'banana']]\nfreqItemSet, rules = apriori(itemSetList, minSup=0.5, minConf=0.5)\nprint(freqItemSet)\nprint(rules)  \n# [[{'beer'}, {'rice'}, 0.6666666666666666], [{'rice'}, {'beer'}, 1.0]]\n# rules[0] --\u003e rules[1], confidence = rules[2]\n```\n\n### Clone the repo\n\nGet a copy of this repo using git clone\n```\ngit clone https://github.com/chonyy/apriori_python.git\n```\n\nRun the program with dataset provided and **default** values for *minSupport* = 0.5 and *minConfidence* = 0.5\n\n```\npython apriori.py -f dataset.csv\n```\n\nRun program with dataset and min support and min confidence  \n\n```\npython apriori.py -f ../dataset/tesco2.csv -s 0.5 -c 0.5\n```\n\n## Concepts of Apriori\n\n* **Support:** Fraction of transactions that contain an itemset\n* **Confidence:** Measures how often items in Y appear in transactions that contain X\n* **Frequent itemset:** An itemset whose support is greater than or equal to a minSup threshold\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchonyy%2Fapriori_python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchonyy%2Fapriori_python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchonyy%2Fapriori_python/lists"}