Using Clustering and Pandas to Find Correlations Among Dating Profiles
A fter swiping endlessly through hundreds of dating profiles and not matching with a single one, one might start to wonder how these profiles are even showing up on their phone. All of these profiles are not the type they are looking for. They have been swiping for hours or even days and have not found any success. They might start asking:
“Why are these dating apps showing me people that I know I won’t match with?”
The dating algorithms used to show dating profiles might seem broken to plenty of people who are tired of swiping left when they should be matching. Every dating site and app probably utilize their own secret dating algorithm meant to optimize matches among their users. But sometimes it feels like it is just showing random users to one another with no explanation. How can we learn more about and also combat this issue? By using a little something called Machine Learning .
We could use machine learning to expedite the matchmaking process among users within dating apps. With machine learning, profiles can potentially be clustered together with other similar profiles. This will reduce the number of profiles that are not compatible with one another. From these clusters, users can find other users more like them. The machine learning clustering process has been covered in the article below:
Dating Algorithms using Machine Learning and AI
Utilizing Unsupervised Learning on User Dating Profiles
Take a moment to read it if you want to know how we were able to achieve clustered groups of dating profiles.