## Inspiration

Currently the insurance claims process is quite labour intensive. A person has to investigate the car to approve or deny a claim, and so we aim to make the alleviate this cumbersome process smooth and easy for the policy holders.

## What it does

Quick Quote is a proof-of-concept tool for visually evaluating images of auto accidents and classifying the level of damage and estimated insurance payout.

## How we built it

The frontend is built with just static HTML, CSS and Javascript. We used Materialize css to achieve some of our UI mocks created in Figma. Conveniently we have also created our own "state machine" to make our web-app more responsive.

## Challenges we ran into

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> I've never done any machine learning before, let alone trying to create a model for a hackthon project. I definitely took a quite a bit of time to understand some of the concepts in this field. *-Jerry*
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## Accomplishments that we're proud of

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> This is my 9th hackathon and I'm honestly quite proud that I'm still learning something new at every hackathon that I've attended thus far. *-Jerry*
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## What we learned

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> Attempting to do a challenge with very little description of what the challenge actually is asking for is like a toddler a man stranded on an island. *-Jerry*
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## What's next for Quick Quote

Things that are on our roadmap to improve Quick Quote:

* Apply google analytics to track user's movement and collect feedbacks to enhance our UI.
* Enhance our neural network model to enrich our knowledge base.
* Train our data with more evalution to give more depth
* Includes ads (mostly auto companies ads).
## Inspiration

As avid readers, we wanted a tool to track our reading metrics. As a child, one of us struggled with concentrating and focusing while reading. Specifically, there was a strong tendency to zone out. Our app provides the ability for a user to track their reading metrics and also quantify their progress in improving their reading skills.

## What it does

By incorporating Ad Hawk’s eye-tracking hardware into our build, we’ve developed a reading performance tracker system that tracks and analyzes reading patterns and behaviours, presenting dynamic second-by-second updates delivered to your phone through our app.
These metrics are calculated through our linear algebraic models, then provided to our users in an elegant UI interface on their phones. We provide an opportunity to identify any areas of potential improvement in a user’s reading capabilities.

## How we built it

We used the Ad Hawk hardware and backend to record the eye movements. We used their Python SDK to collect and use the data in our mathematical models. From there, we outputted the data into our Flutter frontend which displays the metrics and data for the user to see.

## Challenges we ran into

Piping in data from Python to Flutter during runtime was slightly frustrating because of the latency issues we faced. Eventually, we decided to use the computer's own local server to accurately display and transfer the data.

## Accomplishments that we're proud of

Proud of our models to calculate the speed of reading, detection of page turns and other events that were recorded simply through changes of eye movement.

## What we learned

We learned that Software Development in teams is best done by communicating effectively and working together with the same final vision in mind. Along with this, we learned that it's extremely critical to plan out small details as well as broader ones to ensure plan execution occurs seamlessly.

## What's next for SeeHawk

We hope to add more metrics to our app, specifically adding a zone-out tracker which would record the number of times a user "zones out".
## Inspiration

During our research, we found that a lot of people face difficulties in claiming their motor damage in a timely manner. It would be beneficial to have a system for the customer to make claims faster and for insurance providers to set aside the claim cost accurately. Thus, we decided to create an app where the customer can submit claims and receive an estimate repair cost by simply take a picture of the damage. To avoid panics after an accident, we also provide instruction for after-accident care. We want to help facilitate a more efficient claim process system.

## What it does

Our AI-based app can instruct the customer what to do after an accident happens, identify the motor damage parts and severity with our well-trained ML model, provide an estimate repair and claim cost and send a complete report to the insurance provider.

## How I built it

We used both TensorFlow and TensorFlow Lite to train on thousands of collision images we scraped from the internet. We classified them accurately and trained the model to identify them. Then we integrate the trained model into an Android app with Android Studio.

## Challenges I ran into

We have tried different methods to perfect the model. None of us have experience with Android development so we learned along the way

## Accomplishments that I'm proud of

Didn't give up on the last day and finished the project

## What I learned

Tensorflow Lite, Android Studio and teamwork

## What's next for Project Winston - Your Car Insurance Companion

Upgrade the UX and add more functionalities