SnailCare

Inspiration

As skincare enthusiasts we were inspired to make an app that can help others build an easy and simple skincare regimen catered to their needs. We also like snails ✌️🐌

What it does

This web application uses open-source Sephora product data sets to generate a skincare routine for users after a quiz. We are using the reviews of Sephora users similar to the user of our application. We compiled a list of products by normalizing the ratings and averaging them to get the best-rated ones. Many applications use collaborative user recommendations, which significantly help with user experience, especially in e-commerce.

How we built it

We used Python for the backend, React for the frontend and connected the two using Flask.

Challenges we ran into

We initially wanted to scrape the data ourselves, but getting all of it would have taken too long. We also ran into some issues setting up our Flask project. Some of the Python Libraries were not importing correctly. We still have to figure that one out!

Accomplishments that we're proud of

We’re proud that we got to explore new technologies through this project. Although our project is not fully functional, we got to step out of our comfort zone and learn about different frameworks.

What we learned

We gained a more in-depth understanding of full-stack development and how an application's front-end and back-end code interact. This project was one of our first exposure to developing a front-end with React.js, so we learned a lot! Unfortunately, we were unable to complete the project fully as we wanted, but we will keep on debugging and reading documentation to have this running someday.

What's next for SnailCare

We want to generate recommendations based on a price range eventually. We'd also like to get data from different retailers to give a broader range of products/prices. To do so, we would have to scrape data from other websites and normalize the reviews. Collecting the data would also allow us to set more specific skin care categories and provide better recommendations. Making the quiz more detailed to generate more detailed user profiles would also help improve our recommendations!