Crime Analysis

crimes in each district
crimes each year
number of crime precincts in the districts
safest neighbours
count of crimes on different days
The drug alcohol crime region analysis
number of crimes in each month
number of crimes each year
number of crimes in each hour
correlation between different features of the dataframe

Inspiration

It is seen that a hike in the crimes has no end and it becomes very important to incorporate new ways to track down criminals and reduce these crimes. Where each domain of the technology provides help to this problem ranging from surveillance to biometrics, I have tried to bring out some help from the field of data science. I have performed an exploratory data analysis on the dataset of crimes in the city of Denver.

What it does

This in-depth analysis helps to bring out useful insights from the given data. From this analysis, I was able to come to some very useful conclusions like which cities are most prone to crimes, which time of the year, time of the month, and which time of the day is most dangerous. The districts and precincts with the most crimes, the safest and dangerous neighbors. These insights can be used to make useful decisions by the higher authorities to take required actions and increase security.

How I built it

I performed this analysis using python and libraries like pandas, NumPy, matplotlib, seaborn, folium in jupyter lab.

Challenges I ran into

The data was of large size which made it a bit difficult to manage it at first. After that, the choice of plots to better visualize the data was a bit confusing.

Accomplishments that I am proud of

The EDA is capable of giving meaningful insights and provide meaning to the data.

What I learned

I learned different techniques of drawing better insights from the data ranging from different visualization libraries to feature selection and data cleaning.