  Relevant research has been highlighted in the computing community to develop
machine learning models capable of predicting the occurrence of crimes,
analyzing contexts of crimes, extracting profiles of individuals linked to
crime, and analyzing crimes over time. However, models capable of predicting
specific crimes, such as homicide, are not commonly found in the current
literature. This research presents a machine learning model to predict homicide
crimes, using a dataset that uses generic data (without study location
dependencies) based on incident report records for 34 different types of
crimes, along with time and space data from crime reports. Experimentally, data
from the city of Bel\'em - Par\'a, Brazil was used. These data were transformed
to make the problem generic, enabling the replication of this model to other
locations. In the research, analyses were performed with simple and robust
algorithms on the created dataset. With this, statistical tests were performed
with 11 different classification methods and the results are related to the
prediction's occurrence and non-occurrence of homicide crimes in the month
subsequent to the occurrence of other registered crimes, with 76% assertiveness
for both classes of the problem, using Random Forest. Results are considered as
a baseline for the proposed problem.
