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Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA

AI-powered crime prediction

Or Herman-Saffar (Dell), Ran Taig (Dell)
2:40pm3:20pm Wednesday, March 7, 2018
Law, ethics, and governance, Strata Business Summit
Location: 230 A Level: Beginner

Who is this presentation for?

  • Data scientists

Prerequisite knowledge

  • Familiarity with basic machine learning models

What you'll learn

  • Learn how data scientists are using the Crimes in Chicago dataset to find interesting trends and make predictions for the future

Description

What if we could predict when and where next crimes will be committed? Crimes in Chicago, a publicly published dataset of reported incidents of crime that have occurred in Chicago since 2001, contains as many as 6.4 million rows, and each row includes crime type, geographical location, and date and time when the crime occurred. This extensive data source is very valuable and can form the basis for a machine learning model. One direct and immediate motivation for the dataset is making crime counts predictions for specific crimes, which would assist the police in deciding which areas and times to increase their resources, having a concrete impact on citizens’ safety. However, previous work done on this dataset has been mostly descriptive—explorations made at a high level of the current state and counts (i.e., how many crimes have been committed up to a specific point in time)—rather than focused on predictive models.

Or Herman-Saffar and Ran Taig offer an overview of Crimes in Chicago and explain how to use this data to explore committed crimes to find interesting trends and make predictions for the future. Or and Ran conclude by exploring the development of a machine learning model that predicts crime counts for specific crime type on a given day in a specific district within Chicago and cover lessons and insights learned.

Photo of Or Herman-Saffar

Or Herman-Saffar

Dell

Or Herman-Saffar is data scientist at Dell. She holds an MSc in biomedical engineering, where her research focused on breast cancer detection using breath signals and machine learning algorithms, and a BS in biomedical engineering specializing in signal processing from Ben-Gurion University, Israel.

Photo of Ran Taig

Ran Taig

Dell

Ran Taig is a senior data scientist at Dell-EMC, where he leads data science projects, especially in domain of hardware failure prediction, and plays a key roll in designing the team engagement models and work structure, serving as a consultant to EMC’s business data lake team. Ran is also responsible for the team academic relations and continues to teach theory courses for CS students. Previously, Ran served as a lecturer for the design of algorithms course and other CS theory courses for CS bachelors at Ben-Gurion University. He holds a PhD in computer science from Ben-Gurion University, Israel, where he specialized in artificial intelligence. His research mainly focused on automated planning.

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