Inspiration
High costs of healthcare and insurance premiums made us curious to look at the impact of these features even after the rolling out of the iconic Affordability Care Act
What it does
Project classified states into 3 categories based on how well they performed while taking the insurance premiums, cost of healthcare and the number of uninsured people into consider. This classification is done for each economic category.
How we built it
We built the project on Google Colab using Python code. Numpy, pandas, keras, tensor flow were some of the key packages involved in the building of this project!
Challenges we ran into
We ran into a lot of challenges while building the project like hardware configurations and the pressure of midterms at the same time. In terms of hardware, none of our laptops were able to run the tensor flow modules which is why we shifted to Google Colab. Effective prioritization and time management helped us manage the time we had to cope with the pressure of the midterms along the way too.
Accomplishments that we're proud of
Overcoming challenges and producing presentable model and learning new concepts of machine learning along the way.
What we learned
Machine learning concepts and how to plan, develop, and build a project on your own
What's next for Dr. Midas
There are other parameters of insurance that states can be judged on such as deductibles, quality of health care provided which can be used to provide a comprehensive review of states' performance on insurance.
Log in or sign up for Devpost to join the conversation.