Brief (in brief)
In this group project, we performed an exploratory analysis and created an explanatory model of knife crime in London. In addition to this we performed an analysis of stop and searches as a potential threat to the police officers undertaking them. The motivations for this project were prompted by the media coverage of knife crime in London and was performed as a learning exercise into a range of different big data methods:
- Exploratory analysis
- Predictive model creation
- Explanatory model creation
To begin, we had to look at what was the kind of data available to us on our chosen topics. We decided to try and improve our skills in data gathering and so focused on finding a subject that was interesting and was surrounded by misinformation. This meant, however, that it was extremely difficult to find data sets that had relevant information and were large enough in sample size to form accurate approximations. We found, metropolitan police force's open data on a range of subject related to knife crime and worked diligently to create a thorough data set with useful data that we could easily process.
We began to build our predictive and explanatory models. We created hypotheses and then set about writing the code for the first OLS model....
The chief outcome for this project was simply the report. This needed to be concise and show the depth of analysis performed in the project. This report is attached bellow, please enjoy reading it!
The skill set I gained was the other really import outcome. I found that I felt confident in the process and technical skills needed to create a data driven analysis. I have since used these skills in a large number of different projects.
Link to report Download: