## Inspiration

We were inspired to build Hank after reading the sponsor contests and seeing that Vitech was running what we thought was a super challenging problem. We built Hank as a proof-of-concept to show that much of the advice sought from insurance professionals can be accurately generated by a finely tuned model. Considering our interest in machine learning and data visualization, the project seemed like the perfect fit. In the case of Hank, we aimed to generate plan suggestions and prices for a user through a simple survey. We believe a page like this could live on an insurance company's website and provide users with accurate quotes based off a machine learning model the company is able to tweak in real time.

## What it does

Hank provides a broad set of functionality that helps save time and money for both the clients and the life insurance providers.

For clients looking to purchase health insurance, Hank provides an easy introduction to the process with a simple and friendly application that guides the client through a series of questions to gather information such as age, family status, and personal health. With this information, Hank provides an accurate quote for each of the four insurance plans, as well as making a recommendation for which plan is likely the best for them.

Hank also provides an insurance provider facing visualization that allows for the life insurance provider to tweak Hank's suggestions based on business metrics such as life time value (LTV), customer satisfaction, and customer retention, all without re-training the two machine learning models that Hank uses to provide suggestions.

## How we built it

Hank's suggestion system is composed of three modules that work in series to provide the most accurate and useful data to the user.

The first module, the premium estimator, uses the gathered user demographic data to determine what the premiums for each of the four plans (bronze, silver, gold, platinum) would be. This is done with a neural network trained using tensor flow on the insurance data set provided by Vitech for this competition. Using a neural network for a mixture of continuous and discrete data allows Hank to make complex associations between user features and make accurate premium predictions.

The second module is the suggestion module. The suggestion module uses the premium pricing predicted by the premium estimator module as well as the user's demographic data to suggest which of the four plans would be the most suited to them. Because of many-dimensional nature of the data set as well as the fact that there were so many data points to use, a kth-nearest neighbor training model was applied using scipy.

The third module, the business module, is an exciting module that provides value to the life insurance provider by giving fine control over the suggestions that Hank makes to customers using simple metrics. The business module works by combining information from the first and second modules, as well as data from the World Population API to provide a data set that can be modified using simple scaling factors. Using the data-visualization front-end of this module allows the life insurance provider to tweak Hank's suggestions to better align with business goals. This would normally be a very time consuming process as the machine learning that Hank uses to make decisions would have to be re-trained and re-validated, but with the business module, the key components are pre-abstracted away from the machine learning implementation.

## Challenges we ran into

Our first hurdle for Hank was downloading the dataset provided by Vitech. At 1.4m records, it was a difficult task to retrieve and store, especially on a limited connection.

Once our data was successfully scraped, our next challenge was deciding on an appropriate model to use for the machine learning aspect of the project. Many options were tried and discarded, namely Bayseian classification, support vector machines, and random forest classification. By continuously training and testing different models, we ultimately decided on two - a nearest neighbour simulation and a neural network.

## Accomplishments that we're proud of

We're happy to say that we managed to deliver on what we thought would be our two biggest challenges - a pleasing and responsive UI and a meaningful data visualization.

We're especially proud of the business logic control panel which is used to modify and visualize the different goals a company wishes to optimize for.

## What we learned

This project was a chance for us to learn about machine learning as well as test our ability to put ourselves in the shoes of both our client as a software developer and the users of the application, to provide special features to both. The time constraints provided by the hackathon also taught us to manage our time and communicate well - as a team of two people tackling such an ambitious project, constant communication was a must.

## What's next for Hank: Your health insurance advisor

The next step for Hank is to train it against a larger, more diverse dataset and further optimize our models. Once we've trained our model against another set, we'll also be able to add additional steps to the survey, giving us more information to query with. The ultimate goal for Hank would be to host it live on an insurance company's site and see it used by real people and trained with real data.
We were inspired by the daily struggle of social isolation.

Shows the emotion of a text message on Facebook

We built this using Javascript, IBM-Watson NLP API, Python https server, and jQuery.

Accessing the message string was a lot more challenging than initially anticipated.
Finding the correct API for our needs and updating in real time also posed challenges.

The fact that we have a fully working final product.

How to interface JavaScript with Python backend, and manually scrape a templated HTML doc for specific key words in specific locations

Incorporate the ability to display alternative messages after a user types their initial response.
## Inspiration

Everybody struggles with their personal finances. Financial inequality in the workplace is particularly prevalent among young females. On average, women make 88 cents per every dollar a male makes in Ontario. This is why it is important to encourage women to become more cautious of spending habits. Even though budgeting apps such as Honeydue or Mint exist, they depend heavily on self-motivation from users.

## What it does

Our app is a budgeting tool that targets young females with useful incentives to boost self-motivation for their financial well-being. The app features simple scale graphics visualizing the financial balancing act of the user. By balancing the scale and achieving their monthly financial goals, users will be provided with various rewards, such as discount coupons or small cash vouchers based on their interests. Users are free to set their goals on their own terms and follow through with them. The app re-enforces good financial behaviour by providing gamified experiences with small incentives.

The app will be provided to users free of charge. As with any free service, the anonymized user data will be shared with marketing and retail partners for analytics. Discount offers and other incentives could lead to better brand awareness and spending from our users for participating partners. The customized reward is an opportunity for targeted advertising

## Persona

Twenty-year-old Ellie Smith works two jobs to make ends meet. The rising costs of living make it difficult for her to maintain her budget. She heard about this new app called Re:skale that provides personalized rewards for just achieving the budget goals. She signed up after answering a few questions and linking her financial accounts to the app. The app provided simple balancing scale animation for immediate visual feedback of her financial well-being. The app frequently provided words of encouragement and useful tips to maximize the chance of her success. She especially loves how she could set the goals and follow through on her own terms. The personalized reward was sweet, and she managed to save on a number of essentials such as groceries. She is now on 3 months streak with a chance to get better rewards.

## How we built it

We used : React, NodeJs, Firebase, HTML & Figma

## Challenges we ran into

We had a number of ideas but struggled to define the scope and topic for the project.

* Different design philosophies made it difficult to maintain consistent and cohesive design.
* Sharing resources was another difficulty due to the digital nature of this hackathon
* On the developing side, there were technologies that were unfamiliar to over half of the team, such as Firebase and React Hooks. It took a lot of time in order to understand the documentation and implement it into our app.
* Additionally, resolving merge conflicts proved to be more difficult. The time constraint was also a challenge.
## Accomplishments that we're proud of
* The use of harder languages including firebase and react hooks
* On the design side it was great to create a complete prototype of the vision of the app.
* Being some members first hackathon, the time constraint was a stressor but with the support of the team they were able to feel more comfortable with the lack of time
## What we learned
* we learned how to meet each other’s needs in a virtual space
* The designers learned how to merge design philosophies
* How to manage time and work with others who are on different schedules

## What's next for Re:skale

Re:skale can be rescaled to include people of all gender and ages.

* More close integration with other financial institutions and credit card providers for better automation and prediction
* Physical receipt scanner feature for non-debt and credit payments

## Try our product

This is the link to a prototype app
<https://www.figma.com/proto/nTb2IgOcW2EdewIdSp8Sa4/hack-the-6ix-team-library?page-id=312%3A3&node-id=375%3A1838&viewport=241%2C48%2C0.39&scaling=min-zoom&starting-point-node-id=375%3A1838&show-proto-sidebar=1>
 This is a link for a prototype website
<https://www.figma.com/proto/nTb2IgOcW2EdewIdSp8Sa4/hack-the-6ix-team-library?page-id=0%3A1&node-id=360%3A1855&viewport=241%2C48%2C0.18&scaling=min-zoom&starting-point-node-id=360%3A1855&show-proto-sidebar=1>