## Inspiration

as we have spent some time in our new Covid-19 world, we have noticed work from home and such has resulted in an increase of phishing and fraudulent emails. We noticed this is strongly apparent in our educational community as well noticing that our inboxes and our peers' inboxes get spammed with fraudulent E-mails that pose a risk and threat to our finances.

## What it does

MailSafe allows you to verify the integrity and legitimacy of your emails by generating a QR Code in a Google Chrome Extension pop-up for your phone to scan. The QR Code is scanned by the phone in the MailSafe Mobile App that cross references the QR Code to a list of verified email senders. If present on the list, it means the email is verified and the user can trust the email they received. If it is not on the list, the email is most likely fraudulent as it does not belong to any of your financial service providers.

## How we built it

We had used standard Google Chrome Extension structure. We constructed a main manifest file for the Chrome Extension to function and call a popup.html file upon an Chrome Extension Action that showed a button. Clicking that button generated a QR Code from a standard qrcode.js library that your phone could actually scan. Then we created the way the app would function on your mobile device using Figma.

## Challenges we ran into

The main challenge we ran into was in the Google Chrome Extension. We had to use the email sender ID to generate a QR Code. After some trial and error, and countless stackoverflow guides, we came to a library, qrcode.js, that allowed us to pass the email sender ID and generate a QR Code from it. That QR Code is functional and usable by most devices.

## Accomplishments that we're proud of

We are proud of the functional Google Chrome Extension that generates actual QR Codes for devices to scan. All in all, we are proud to have come up with an idea to protect users from fraudulent E-mails and phishing scams and proud that we have implemented a major element of the system. Now that we have that foundation, we are on our way to complete the mobile application element to have a functional system.

## What we learned

The project was a great learning experience for us at MailSafe. Spending most of our time implementing the Google Chrome Extension we learned to manipulate an existing HTML file, parse it and extract information we specifically needed such as the email sender ID. Using that we learned how to convert it into a QR Code that actually is recognized by anyone's cellular device. All in all, it was an amazing opportunity and learning experience.

## What's next for MailSafe

Our goals for MailSafe is to implement a functional phone application. Furthermore, we would like to implement the the extension add-on for non-chromium based browsers such as Mozilla Firefox. In the future, we are looking forward to wide-spread adoption by many companies, not just Bank Institutions, who would like to provide their users with a seamless and safe experience in communicating over E-mail.
## 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

With the recent Corona Virus outbreak, we noticed a major issue in charitable donations of equipment/supplies ending up in the wrong hands or are lost in transit. How can donors know their support is really reaching those in need? At the same time, those in need would benefit from a way of customizing what they require, almost like a purchasing experience.

With these two needs in mind, we created Promise. A charity donation platform to ensure the right aid is provided to the right place.

## What it does

Promise has two components. First, a donation request view to submitting aid requests and confirm aids was received. Second, a donor world map view of where donation requests are coming from.

The request view allows aid workers, doctors, and responders to specificity the quantity/type of aid required (for our demo we've chosen quantity of syringes, medicine, and face masks as examples) after verifying their identity by taking a picture with their government IDs. We verify identities through Microsoft Azure's face verification service. Once a request is submitted, it and all previous requests will be visible in the donor world view.

The donor world view provides a Google Map overlay for potential donors to look at all current donation requests as pins. Donors can select these pins, see their details and make the choice to make a donation. Upon donating, donors are presented with a QR code that would be applied to the aid's packaging.

Once the aid has been received by the requesting individual(s), they would scan the QR code, confirm it has been received or notify the donor there's been an issue with the item/loss of items. The comments of the recipient are visible to the donor on the same pin.

## How we built it

Frontend: React  
Backend: Flask, Node  
DB: MySQL, Firebase Realtime DB  
Hosting: Firebase, Oracle Cloud  
Storage: Firebase  
API: Google Maps, Azure Face Detection, Azure Face Verification  
Design: Figma, Sketch

## Challenges we ran into

Some of the APIs we used had outdated documentation.
Finding a good of ensuring information flow (the correct request was referred to each time) for both the donor and recipient.

## Accomplishments that we're proud of

We utilized a good number of new technologies and created a solid project in the end which we believe has great potential for good.

We've built a platform that is design-led, and that we believe works well in practice, for both end-users and the overall experience.

## What we learned

Utilizing React states in a way that benefits a multi-page web app
Building facial recognition authentication with MS Azure

## What's next for Promise

Improve detail of information provided by a recipient on QR scan. Give donors a statistical view of how much aid is being received so both donors and recipients can better action going forward.

Add location-based package tracking similar to Amazon/Arrival by Shopify for transparency