105_CalBurnIQ

Inspiration

The motivation for the project is to utilize AI to replace traditional IoT-based health tracking devices, in order to eliminate the cost of the hardware and make the associated web app more accessible to a wider range of users, given the widespread availability of internet access.

What it does

The application will predict the number of calories burned during exercise based on input from the user such as their BMI, heart rate, and exercise duration. Users will also have the option to log in and access the app's features through authentication provided by Firebase.

How we built it

We began by constructing a prediction model using the random forest algorithm. We then used the Streamlit library in Python to create a web interface with a visually appealing frontend. We also incorporated Firebase code, API keys, and user login functionality, as well as creating various routes for the application. Finally, we put everything together to create the final product.

Challenges we ran into

One of the significant challenges we faced during the project was how to effectively run the model on the server-side to minimize load on the client end. Once we found a solution in the form of Streamlit, we had to carefully study its documentation and integrate it with our Firebase code without compromising the accuracy of the model.

Accomplishments that we're proud of

We were able to complete all the planned functionalities of the project within a day. There are still additional features that we plan to implement in the future, but for the time being, we are confident that we have created a useful product

What we learned

During this project, we acquired a range of technical skills including the use of Streamlit and Firebase with Python, as well as the implementation of machine learning in a web application. In addition to that, we also gained experience in working efficiently as a team to complete the task at hand.

What's next for 105_CalBurnIQ

With regards to future prospects, we are optimistic about expanding the capabilities of this project. We intend to implement the ability to save and track users' exercise history and also plan to make our current dataset more dynamic by incorporating data from a growing number of users who use our app