Missing Values:
I have found few missing values in the dataset, the below image gives number of missing values in each attribute
There are total of 8002 instances with 17 attributes, out of which, most of the missing values are present in the ‘race’ attribute. In future I will try to remove the missing values, either I may remove the missing values or handle with few techniques.
State wise victims:
I have gone through the dataset and I have found that there are total of 51 different states in the dataset. I have started my analysis with an assumption that the most of the victims will be in the state with highest population compared to other states.
From the above visualization we can say that the California state has the highest police shootings and the Rhode Island state has the lowest police shootings. More the population, more the crimes and a greater number of police stations.
Genders as victims:
After the going through the state wise victims, I have found an interesting attribute ‘Genders’, I wanted to explore which gender has more frequently been the target or victim.
After the analysis I can say that almost 95.7% of the data male has more frequently been the target or victim, and the female count is less than 400 instances.
Victims with Mental Illness:
Another interesting attribute I found is ‘Mental Illness’, I have stared exploring this attribute with an assumption that there will be significant percentage of victims with mental illness.
In the above visualization True indicates there are signs of mental illness and False indicates that there no signs of mental illness. From the visualization I can say that only 20% of cases are under the signs of mental illness and remaining 80% percent are mentally stable victims.