I've heard people saying that we are not sustainable and we may get affected to diseases sooner and our life span is less and so on, so I decided to get a ML Model predict same using the real time feeds of people living in a particular area and the ratio of people affected.
What it does
It's an ML Model that takes in feeds like new cases, population density, deaths , etc in a particular area and predicts the average life sustainability of a person living in that area. I'm not sure, how much this is going to help people, but yes, it will remove the unnecessary "We are not sustainable" voice of many.
How I built it
I build it using Python, Jupyter Notebook. I created the ML Model and trained it using a public Covid Dataset and then deployed it to work through the prediction process.
Challenges I ran into
ML Model failed to get accurate and deployment using flask was done by me for the first time, hence I went though a lot a bugs and errors in making my model work and predict as expected.
Accomplishments that I'm proud of
At the beginning of this hackathon, I was not sure what I am going to build, and I was not even sure how/whether I should team up as I knew no one. So I decided to give it a try myself and here, after a day, almost ready to deploy a working model.
What I learned
What's next for Life Sustainability Predictor
I'll try to train the model better to predict better and deploy it in GCloud. I did deploy to certain extent but due to billing issue s, the files got scattered.