Along with swiggy, we also worked with CRED. To have a little brief about CRED, it’s a member-only-club company that rewards individuals for their timely credit card bill payments by providing them with exclusive offers and access to premium experiences.

What we did :

For the first problem statement:

we focused on two major paradigms of recommendation systems for user engagement, collaborative filtering and content based models. We started with creating our own synthetic datasets, one which generated latitude and longitude using random numbers (which was our location data), for a given seed point we generated 100 more.

Other, based solely on the past interactions recorded between users and items. Our overall motive was to do collaborative and content based filtering based on the radius of the points based on the user-item interactions of the second data set. We tried indulging Memory based approaches that directly works with n values of recorded interactions of content based methods, based on the available “features”.

For the second problem statement:

We’ve created a working model with a great UI to represent the visualization of the data which benefits the idea of rewards based on location and preferences. For the second problem, we made an Credit Card Scanner app which used Measurement, Vision Image label, Stats, Scanner APIs, which was completely built on Firebase using Kotlin.

We were little inaccurate in context to scanning accurate data from the credit card, (say5 out of 10 were true), yet we found out many interesting base problems that can be solved.Like, color and background of the card, font of the numbers etc.

Finally, presented it with a UI that involves details to scan instead of the traditional type system.In the end, the client was happy with our approaches and use cases.