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SUMMARY

In the Baltimore Crime Dataset, I have created (8) different type of Visualizations are as follows:

The 1st visualization describes about the relationship between Weapon Used and its associated count of incident occurrence. So, here we can see that hands are used more as a weapon for committing the crime.(Here i’m not considering the N/A variable as it doesn’t provide any information to us)

The 2nd visualization represents the stacked distribution of different types of crime over the years.So, here we can interpret that year 2016 accounts for most number of crimes with their associated crime types.

The 3rd visualization shows the relationship of types of crimes with respect to the weapon used. Here we can conlude that hand is used as a weapon to the commit the Assualt Crime.(Here i’m not considering the N/A variable as it doesn’t provide any information to us)

The 4th visualization highlights the density distribution of types of crime based on time of crime happen. Here we can conclude that most of the crime happen in night/evening.

The 5th visualization is the mapping of types of crimes in a Baltimore based on the longitude & latitude coordinates.

The 6th visualization represents the number of crime incident happen based on their location.For example, whether the incident happen inside or outside. So, here we can see that there is not much different has been noted. The crime incident happen ‘Outside’ is slightly more than crime incident happen ‘Inside’.

The 7th Visualization represents the Distribution of Crime Incidents based on its District. Here, Northeastern records the highest number of crime incident.

The 8th visualization represents the count of crime incidents happen as per their premise. For example, here we can see that most number of crime happen on the street whereas school accounts for lowest crime incident. Moreover, in this visualization we have consider Top 5 Premise location from the dataset.