In this project, our goal was to analyze a dataset of crime evidence and identify the most likely suspect. Using data analysis and custom scripts, our team determined that Ruth was the primary suspect.
- Alibi check: Ruth claimed she was out of town all week, but phone location data showed her within ¾ mile of two crime scenes and on Linden Street during a reported break-in.
- Property ownership: She owned every home that was broken into, giving her possible inside access and knowledge of tenant routines.
- Program results: After running our analysis scripts, Ruth was consistently ranked as the top suspect.
I worked on a team of three to write scripts that analyzed and compared data points such as phone records, property ownership, and timelines. I also developed a Python-based user interface to visualize the data and make the results easier to interpret. Our combined analysis led us to identify Ruth as the most likely suspect and earn 2nd place in the competition.