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Hosted by: Brandi Kiehl | 👥 1 attendee
Combined Group Event
**Implementing Multivariate Anomaly Detection in Python**
Anomaly detection helps us find data points which are inconsistent with the rest of a dataset. But what does this mean when our data points have many individual features? In other words, how can we tell whether a data point is an outlier when we have dozens of input columns? In this talk, you will learn about several techniques designed to solve this problem. We will understand the intuition and math behind these techniques, implement a simple outlier detector in Python incorporating these algorithms, and create a Streamlit app to host the detector.
Kevin Feasel is a Microsoft Data Platform MVP and CTO at FareGame Inc, where he specializes in data analytics with T-SQL and R, forcing Spark clusters to do his bidding, fighting with Kafka, and pulling rabbits out of hats on demand. He is the lead contributor to Curated SQL ([https://curatedsql.com](https://curatedsql.com)), president of the Triangle Area SQL Server Users Group ([https://www.meetup.com/tripass](https://www.meetup.com/tripass)), and author of PolyBase Revealed ([https://www.apress.com/us/book/9781484254608](https://www.apress.com/us/book/9781484254608)). A resident of Durham, North Carolina, he can be found cycling the trails along the triangle whenever the weather's nice enough.
Meeting will be in person at Akkodis 6th floor Conference Rooms and streamed online.
This event is both in person and online:
To join the video meeting, click this link: https://meet.google.com/ibk-jaep-fvp
Otherwise, to join by phone, dial +1 929-266-2039 and enter this PIN: 990 829 681#
Tentative Schedule:
6-6:30 PM Doors open, setup, eat, drink, network, and allow time for everyone to arrive
6:40 PM Announcements
6:50 PM Presentation
7:45-8 PM Q&A, Closing comments, network and adjourn
Thank you to Akkodis for hosting and sponsoring.
Note:
This should go without saying, but please respect their space. We ask that you do not touch their displays, servers, work areas, etc.