Tailor Matched is an algorithm to predict what item a customer would use based on customer data. Used Tailor Brands' data to train model on 300k+ data points of past customers and the items that they purchased. Using a logistic regression, we were able to produce 36% accuracy on our recommendations. Our algorithm is designed to be applied to Tailor Brands' recommendation system as the recommendation engine based on customer profiles.

We used a TypeForm survey and integrated their API to have a live demo, where we let judges input their own data as prospective customers, and the algorithm would produce a recommendation for them.

Winner of HackSC's Best Use of Tailor Brands' data. This was a joint project with Harsh Chobisa, Anirudh Mani, and Tejas Bhat.

To see the DevPost, click here