DataScience: Association Rule Learning in Python
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Want to learn a really powerful data science skill?
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large datasets. It is an often overlooked or forgotten method in the data science, machine learning, and python communities.
Association Rule Learning has a number of interesting business and science applications. It is commonly used in market basket analysis rules, Web usage mining, intrusion detection, continuous production, and bioinformatics
{ Zax Rosenberg, Python } => { A really good talk! }
Zax will walk through his recent project, which leveraged Associatino Rule Learning. In doing so, he will discuss the techniques and tools (i.e. apriori, fp-growth, etc.) used.
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Zax Rosenberg is currently an equity research associate for Robert W. Baird & Co., covering transportation and logistics, including integrators, railroads, third-party logistics, transport leasing, truckload carriers, and less-than-truckload carriers. Prior to joining Baird, Zax spent four years in the single family office industry - most recently as a Senior Investment Analyst, where he sourced, evaluated, selected, and monitored long only funds, hedge funds, private equity funds, and direct investments, across asset classes. He was also responsible for managing multi-billion dollar portfolios' allocations and risk. Additionally, Zax served as Chairman of the Board for the Dean's Advisory Council for Roosevelt University's Walter E. Heller College of Business Administration for three years. He graduated with highest honors from Roosevelt University with a BS in finance, and is a Chartered Financial Analyst.
In his free time, Zax is a hobbyist python developer (@zaxr on github), blogger (zaxrosenberg.com), and gamer.