%0 Journal Article
	%A Luis Fonseca and  Filipe Cabral Pinto and  Susana Sargento
	%D 2021
	%J International Journal of Computer and Systems Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 170, 2021
	%T An Application for Risk of Crime Prediction Using Machine Learning
	%U https://publications.waset.org/pdf/10011881
	%V 170
	%X The increase of the world population, especially in
large urban centers, has resulted in new challenges particularly
with the control and optimization of public safety. Thus, in the
present work, a solution is proposed for the prediction of criminal
occurrences in a city based on historical data of incidents and
demographic information. The entire research and implementation
will be presented start with the data collection from its original
source, the treatment and transformations applied to them, choice and
the evaluation and implementation of the Machine Learning model up
to the application layer. Classification models will be implemented to
predict criminal risk for a given time interval and location. Machine
Learning algorithms such as Random Forest, Neural Networks,
K-Nearest Neighbors and Logistic Regression will be used to predict
occurrences, and their performance will be compared according
to the data processing and transformation used. The results show
that the use of Machine Learning techniques helps to anticipate
criminal occurrences, which contributed to the reinforcement of
public security. Finally, the models were implemented on a platform
that will provide an API to enable other entities to make requests for
predictions in real-time. An application will also be presented where
it is possible to show criminal predictions visually.
	%P 166 - 174