Peedom is an app that helps users in Berlin find public toilets in their area. The app uses filters to customize the search based on the user's needs. The team behind the project consisted of 5 members, and they used various tools and technologies like Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Flask, HTML5, CSS3, Bootstrap5, Javascript, and Google Maps API to develop the app.
The data science team collected and cleaned the data on public toilets in Berlin and used the K-Means clustering algorithm to group the toilets based on their location distance. They also created a rating system for the toilets based on their attributes like accessibility and Google ratings.
The app's back-end was built using Node and hosted on a separate server, while the front-end was built using HTML, CSS, and Bootstrap5, and Google Maps API was used to provide the main map layout.
The Peedom project aims to solve the problem of finding public toilets in Berlin, which is especially challenging for women who have to pay for their use. The app is a useful tool for people who spend time outdoors and want to find a public toilet quickly and easily.
As a member of the data team, I played an integral role in building the Flask app and other components of the project. We faced numerous challenges along the way, but through collaboration and hard work, we were able to overcome them and deliver a high-quality product. I'm proud to have been part of this project and to have contributed to its success.