Inspiration

We were inspired by looking at the linking of the DataMap that was given to us by Essity and thought that this was restricting in the nature that the IoT data could be utilized. Being able to cross over from the Vru_log to the Scu_log is something that really interested us as a team.

What it does

This creates visualizations to highlight the demand of when people going into these locations with people counters compared to the number of employees completing a task and when. Then, it uses this usage data to predict refills and see if this differs across different variables to inform more optimized cleaning and see how the usage changes across these attributes.

How we built it

We wrote all of the code in Python and made the data visualizations in Tableau in order to add more color and better graphics to our presentation.

Challenges we ran into

We were challenged by linking the Vru_log to the Scu_log given the tough date formatting.

Accomplishments that we're proud of

We are proud that we were able to work together not only as an athletics team, but as a data science team to come up with useful insights for Essity. We believe that these are extremely actionable and are proud of the innovation in our idea given the IoT data that was prepared.

What we learned

We learned how difficult it can be to work across different files as most other competitions/projects in classes had been across only one file, so this really improved our data linking ability. Additionally, there seem to be issues with the thresholding that had been set in the location data.