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Using pandas and scikit-learn together can be a bit clunky. For complex preprocessing, the scikit-learn Pipeline conveniently chains together transformers. But, it will convert your DataFrame to a numpy array. In this talk, we will walk through pandas DataFrames, scikit-learn preprocessing and Pipelines, and how to use custom transformers to stay in pandas land.
About the Speaker
Julie Michelman (https://pydata.org/seattle2017/speaker/profile/68/) is a Data Scientist at zulily, where she focuses on Machine Learning and A/B Testing. Her language choices are versatile, including Python (sklearn, pandas), R (caret, shiny, data.table), Spark (MLlib, Spark SQL + DataFrames), and plenty of SQL. She collaborates with stakeholders across Tech, Marketing, Supply Chain, and other areas of the business.
Julie previously worked as a Data Scientist at CDK Global. She holds a MS in Statistics from the University of Washington and a BA with major in Mathematics from Carleton College.
Metis (thisismetis.com) accelerates careers in data science by providing full-time immersive bootcamps, evening part-time professional development courses, online resources, and corporate programs based in Seattle, New York, Chicago, and San Francisco.
Brought to you by Kaplan, Metis focuses primarily on Python, machine learning, data visualization, deep learning, big data processing, statistical foundations, and more. Students and alumni of the bootcamp program receive continuous support from our career advisors, empowering them to pursue a successful career in the fast-growing field of data science.
Learn more about us at https://thisismetis.com. (https://thisismetis.com/)
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