Communities and Crime Data Set
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The Benchmark has the following main features:
• This is a regression task and the quantity to predict is the ‘Per Capita Violent Crimes’.
• Data have 128 features which are real values, no categorical attribute is present. The features involve the community, such as the percent of the population considered urban and the median family income, and law enforcement, such as per capita number of police officers and percentage of officers assigned to drug units.
• All numeric data are normalized into the decimal range 0-1.
• There are missing values which need to be handled.
• Statistics of features are also included.
• It is easy to download and use, instructions can be found at the link of the dataset.
• This is a regression task and the quantity to predict is the ‘Per Capita Violent Crimes’.
• Data have 128 features which are real values, no categorical attribute is present. The features involve the community, such as the percent of the population considered urban and the median family income, and law enforcement, such as per capita number of police officers and percentage of officers assigned to drug units.
• All numeric data are normalized into the decimal range 0-1.
• There are missing values which need to be handled.
• Statistics of features are also included.
• It is easy to download and use, instructions can be found at the link of the dataset.
Scientific Area:
Machine Learning
Language/Environments:
C, C++, MatLab, Octave, Python, R
Target Group:
Advanced
Cite as:
Redmond, M. and Baveja A., A data-driven software tool for enabling cooperative information sharing among police departments, European Journal of Operational Research 141.3 (2002): 660-678.
Author of the review:
Giulia Cademartori
University of Genoa
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Simone Minisi