## Inspiration

When we first read Vitech's challenge for processing and visualizing their data, we were collectively inspired to explore a paradigm of programming that very few of us had any experience with, machine learning. With that in mind, the sentiment of the challenge themed around health care established relevant and impactful implications for the outcome of our project. We believe that using machine learning and data science to improve the customer experience of people in the market for insurance plans, would not only result in a more profitable model for insurance companies but improve the lives of the countless people who struggle to choose the best insurance plans for themselves at the right costs.

## What it does

Our scripts are built to parse, process, and format the data provided by Vitech's live V3 API database. The data is initially filtered using Solr queries and then formatted into a more adaptable comma-separated variable (CSV) file. This data is then processed by a different script through several machine learning algorithms in order to extract meaningful data about the relationship between an individual's personal details and the plan that they are most likely to choose. Additionally, we have provided visualizations created in R that helped us interpret the many data points more effectively.

## How we built it

We initially explored all of the ideas that we had regarding how exactly we planned to process the data and proceeded to pick Python as a suitable language and interface in which we believed that we could accomplish all of our goals. The first step was parsing and formatting data after which we began observing it through the visualization tools provided by R. Once we had a rough idea about how our data is distributed, we continued by making models using the h2o Python library in order to model our data.

## Challenges we ran into

Since none of us had much experience with machine learning prior to this project, we dived into many software tools we had never even seen before. Furthermore, the data provided by Vitech had many variables to track, so our deficiency in understanding of the insurance market truly slowed down our progress in making better models for our data.

## Accomplishments that we're proud of

We are very proud that we got as far as we did even though out product is not finalized. Going into this initially, we did not know how much we could learn and accomplish and yet we managed to implement fairly complex tools for analyzing and processing data. We have learned greatly from the entire experience as a team and are now inspired to continue exploring data science and the power of data science tools.

## What we learned

We have learned a lot about the nuances of processing and working with big data and about what software tools are available to us for future use.

## What's next for Vitech Insurance Data Processing and Analysis

We hope to further improve our modeling to get more meaningful and applicable results. The next barrier to overcome is definitely related to our lack of field expertise in the realm of the insurance market which would further allow us to make more accurate and representative models of the data.
## Inspiration

According to a 2015 study in the American Journal of Infection Control, people touch their faces more than 20 times an hour on average. More concerningly, about 44% of the time involves contact with mucous membranes (e.g. eyes, nose, mouth).

With the onset of the COVID-19 pandemic ravaging our population (with more than 300 million current cases according to the WHO), it's vital that we take preventative steps wherever possible to curb the spread of the virus. Health care professionals are urging us to refrain from touching these mucous membranes of ours as these parts of our face essentially act as pathways to the throat and lungs.

## What it does

Our multi-platform application (a python application, and a hardware wearable) acts to make users aware of the frequency they are touching their faces in order for them to consciously avoid doing so in the future. The web app and python script work by detecting whenever the user's hands reach the vicinity of the user's face and tallies the total number of touches over a span of time. It presents the user with their rate of face touches, images of them touching their faces, and compares their rate with a **global average**!

## How we built it

The base of the application (the hands tracking) was built using OpenCV and tkinter to create an intuitive interface for users. The database integration used CockroachDB to persist user login records and their face touching counts. The website was developed in React to showcase our products. The wearable schematic was written up using Fritzing and the code developed on Arduino IDE. By means of a tilt switch, the onboard microcontroller can detect when a user's hand is in an upright position, which typically only occurs when the hand is reaching up to touch the face. The device alerts the wearer via the buzzing of a vibratory motor/buzzer and the flashing of an LED. The emotion detection analysis component was built using the Google Cloud Vision API.

## Challenges we ran into

After deciding to use opencv and deep vision to determine with live footage if a user was touching their face, we came to the unfortunate conclusion that there isn't a lot of high quality trained algorithms for detecting hands, given the variability of what a hand looks like (open, closed, pointed, etc.).
In addition to this, the CockroachDB documentation was out of date/inconsistent which caused the actual implementation to differ from the documentation examples and a lot of debugging.

## Accomplishments that we're proud of

Despite developing on three different OSes we managed to get our application to work on every platform. We are also proud of the multifaceted nature of our product which covers a variety of use cases. Despite being two projects we still managed to finish on time.
To work around the original idea of detecting overlap between hands detected and faces, we opted to detect for eyes visible and determine whether an eye was covered due to hand contact.

## What we learned

We learned how to use CockroachDB and how it differs from other DBMSes we have used in the past, such as MongoDB and MySQL.
We learned about deep vision, how to utilize opencv with python to detect certain elements from a live web camera, and how intricate the process for generating Haar-cascade models are.

## What's next for Hands Off

Our next steps would be to increase the accuracy of Hands Off to account for specific edge cases (ex. touching hair/glasses/etc.) to ensure false touches aren't reported. As well, to make the application more accessible to users, we would want to port the application to a web app so that it is easily accessible to everyone. Our use of CockroachDB will help with scaling in the future. With our newfound familliarity with opencv, we would like to train our own models to have a more precise and accurate deep vision algorithm that is much better suited to our project's goals.
## Inspiration

We were heavily focused on the machine learning aspect and realized that we lacked any datasets which could be used to train a model. So we tried to figure out what kind of activity which might impact insurance rates that we could also collect data for right from the equipment which we had.

## What it does

Insurity takes a video feed from a person driving and evaluates it for risky behavior.

## How we built it

We used Node.js, Express, and Amazon's Rekognition API to evaluate facial expressions and personal behaviors.

## Challenges we ran into

This was our third idea. We had to abandon two major other ideas because the data did not seem to exist for the purposes of machine learning.