Overall, write a coherent narrative that tells a story with the data as you complete this section.

Crime affects us all. It affects our friends, our families and our co-workers. More importantly, it affects how we grow as a community. The stronger the community, the more likely it will be able to help its members grow.

There are many types of crimes and have been recorded in many different common areas. These areas include our neighborhood streets, our residence, apartment builds, libraries, schools etc. And a few examples of the types of crimes in these areas are: Robbery, Sexual Assault, Narcotics and many more.
In Chicago, we have record counts in all of these categories. These categories all seem to have a correlation to our factors of growth. Factors of growth such as our community graduation or more explicitly the school drop out rate.

This research project tells the story of how crime and the growing rate of school drop outs are related. Even though the data analyzed for crime analysis only included the years 2012 and 2013, it shows how growth in crime correlates to the growth in drop out rates in those areas where crime is high. More specifically in areas that are less fortunate in community growth. After completing this research and further analyzed the visualizations, I found there are a few areas that are resistant to this crime to drop out rate relationship. However, this is due to its community members having more resources than those with less. I can confirm due to being a native of Chicago and know the areas very well.

Summarize the problem statement you addressed.

Chicago crime growth rates affect school drop out rates and as the drop out rate increase the crime rate also increases.

Summarize how you addressed this problem statement (the data used and the methodology employed).

  1. Using data from the below listed sources, I was able to cleanse, impute, pivot and aggregate to materialize new variables, evaulate them to identify correlating relationships to generate a usable master analytics table.

https://www.kaggle.com/chicago/chicago-police-stations

https://www.kaggle.com/chicago/chicago-public-schools-data

https://www.kaggle.com/chicago/chicago-crime

  1. Using the master analytics table or data frame, I was then able to use the kmeans clustering algorithm to identify specific groups. These groups can then be used to assign tasks forces for each area to help reduce crime.

Summarize the interesting insights that your analysis provided.

This analysis project was able to identify a direct relationship between crime rates and drop out rates as well as the inverse that show as drop out rates grow - crime grows in specific areas of Chicago. It was able to also identify the segemented areas where crimes would be more likely to occur on our neighbor streets by Police District.

Summarize the implications to the consumer (target audience) of your analysis.

This research uses data collected with no intended reporting bias. As is intended to help highlight areas of growth opportunity at the community level. The results of this analysis may cause disparate impact to the residents of areas where crime counts are high if used by a party who’s intension is not fair.

Discuss the limitations of your analysis and how you, or someone else, could improve or build on it.

The limitation of this analysis is due to the limited amount of drop out rate data and is the reason why it only uses data from the year 2012 and 2013. If someone were to have more years of dropout rate by school it may help show more upto date growth in drop out rates.