{ "info": { "author": "Bassim Eledath, Lynn He, Christine Zhu, Amanda Ma", "author_email": "bassimfaizal@gmail.com", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3" ], "description": "\n# \"bizkit\"\n\n\n# bizkit\n\nbizkit is a Python package to help streamlining business analytics data mining tasks. This package provides methods for market basket analysis, anomaly detection, customer survival analysis, customer clustering, and uplifting analysis. Implemented algorithms include mlxtend.apriori, sklearn.IsolationForest, lifelines.KaplanMeierFitter, [], and []. bizkit focuses on ease of use by providing a well-documented and consistent interface. The results are visualized via bokeh library, d3fgraph library, and [].\n\n\n# Reference\n\nMarket basket analysis:\n- Introduction to Market Basket Analysis in Python https://pbpython.com/market-basket-analysis.html\n- A Gentle Introduction on Market Basket Analysis \u2014 Association Rules https://towardsdatascience.com/a-gentle-introduction-on-market-basket-analysis-association-rules-fa4b986a40ce\n\n\n\nAnomaly detection:\n- Anomaly Detection Principles and Algorithms (2017) by Kishan G. Mehrotra, Chilukuri K. 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