Inspiration

We wanted to learn more about our cycles and be able to use it to help us during our daily lives, and found that there weren't many apps that supported this, so we were inspired to build one ourselves. We wanted to make a period tracker that doesn’t just track your period. It tracks all your cycles and helps you understand your body’s rhythm.

What it does

The purpose of our website is to erase the stigma and trepidation surrounding menstruation by gaining a better understanding of yourself and your cycle through bulletin board-esque interactions.

It allows you to find out what activities and diet is best for you during your different phases and track your hormonal levels, cycle insights, productivity levels to help you understand your body and become your best self.

It acts as a daily check-in, guiding you on when to take things light or go heavy, when to plan intense workouts or schedule more demanding tasks, so you can align your lifestyle with your natural energy flow.

How we built it

We built it using react as the front end, node.js as the backend, figma to design and collaborate with members, postgres as our database, using cloud service from aws, auth0 for user authentication to log in and save your information.

Challenges we ran into

Integrating the database with our front end elements

Accomplishments that we're proud of

Exploring different frameworks and using them all together into one project, being able to put them together in the limited time frame in the hackathon.

What we learned

For some of us it was out first time working with the new frameworks, so we learnt a lot of information and how to work with teammates.

Built With

  • amazon-web-services
  • auth0
  • authentication
  • figma
  • figma-to-design-and-collaborate-with-members
  • node.js
  • node.js-as-the-backend
  • postgres-as-our-database
  • postgresql
  • react
  • react-as-the-front-end
  • user
  • using-cloud-service-from-aws
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Updates

posted an update

In addition to the features shown in the video, we also worked on implementing an ensemble supervised learning model using scikit-learn, numpy, and pandas, which would take in data from the user like current symptoms, average period duration, average cycle length, in order to predict the user's next period phase. Through the machine learning model, we were hoping to personalize the user's experience to their cycle, allowing for an even better understanding of their cycle and its phases.

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