The objective of this work is to take advantage
of machine learning to perform exploratory analysis of
historical data and to forecast crime counts in a given
month and year for a 4 year period between 2018 and
2021 and allow the evenly distribution and allocation of
resources and logistics in the case of Ghana. The
prediction was done using the Chicago crime dataset. The
prediction was done using the Facebook prophet. The
month February is the month with the least crime rate
and this can be attributed to the fact that it has fewer days
in the year. It was also discovered that crimes are
committed between the hours of 5:00pm and 10:00 pm
while most of the crimes are committed at 12:00 noon.
With regard to District level crimes, it was observed that
District 11 is the district with the highest crime between
2012 and 2017. This is followed by 7, 4, 25 and 6. This is
an indication that more logistics and personnel will be
required in those Districts to help prevent crimes from
being committed.
The model predicted a decrease in the number of
crimes that are likely to be committed with 2021
recording the least crimes. The model also predicted the
least crimes to be in the period 1st to 30th January, 2021
as 10978 as compared to 1st to 30th January, 2021with the
least crimes committed as 30 in the historical data. Most
crimes happen on street and on sidewalks therefore extra
police personnel needed on street patrolling. A lot of
crimes are in residence and/or apartments therefore the
Police Service will require more personnel to respond to
destress 911 calls from people. The overall trend is that
the crime rate keeps decreasing from the forecast in each
year. The results indicate the importance of the
application of Machine Learning for the prediction of
crime data by the Ghana Police Service. In conclusion,
this work provides the institution with much information
and intuition on the use and application of machine
learning to enhance the decision making process and the
fight against crimes
Keywords : Ghana Police Service, Machine Learning, Artificial Nueral Network, Predictive Policing
