dc.contributor.author	Le, Na	en
dc.contributor.author	Ikeda, Atsushi	en
dc.contributor.author	Sturgis, Brandon	en
dc.contributor.author	Goyal, Uditi	en
dc.date.accessioned	2022-05-13T15:22:00Z	en
dc.date.available	2022-05-13T15:22:00Z	en
dc.date.issued	2020-05-04	en
dc.description.abstract	With millions of crime offenses are recorded each year in the United States, regardless of our position or background in the community, safety and security are our top concern when we are in an area. To help increase the community's confidence in public safety, our team proposed a product that can help people view the crime history as well as predict future times and places for crimes in the Washington D.C. area. This product is designed for a typical worker, a real estate agent, a regular student, a researcher or an analyst that are interested in exploring the likelihood of crime to occur in the area to ensure safety. The product provides users with analysis on the count of records in yearly cases or in each ward in the metropolitan area, analysis on how different machine learning models can effectively classify and predict the offense group. Our product also provides time series analysis in each census tract and thus, can predict the crime trend in the same census tract in the upcoming month.	en
dc.identifier.uri	http://hdl.handle.net/10919/110087	en
dc.language.iso	en_US	en
dc.rights	CC0 1.0 Universal	en
dc.rights.uri	http://creativecommons.org/publicdomain/zero/1.0/	en
dc.subject	crime data mining	en
dc.subject	crime data	en
dc.subject	machine learning	en
dc.subject	time series analysis	en
dc.subject	public safety	en
dc.subject	Washington D.C.	en
dc.title	Crime Data Mining for D.C. area	en
dc.type	Master's project	en

Crime Data Mining for D.C. area

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