## Inspiration

Since its emergence in December of 2019, the COVID-19 pandemic continues to change the lives of the global population. In Canada, the daily case rate has increased eightfold since last October. Financial security and physical health have became the top priorities of people around the world. We are aiming to protect both and giving solutions for the community during the challenging era of Covid-19.

## What it does

The Protect-21 application sends a friendly reminder notification to the user to wear their mask every time they leave / depart from key locations (such as their home and place of business). Our application will also encourage the proper wear of their mask and verify them to minimize the risk of exposure. This app promotes both safe practice of wearing a mask during this pandemic and helps the user to avoid preventable costs, such as buying an overpriced, single use mask, or paying a fine for violating a mask mandate, such as those seen in British Columbia aboard transit vehicles. To further encourage hygiene, a key factor in preventing the contraction of the coronavirus, the user receives a friendly reminder notification once they return to a key location to wash their hands for 20 seconds.

## How we built it

Technologies used:

APIs used : Google Login, Firebase, Maps, Geolocation, Trusted Web Activity, Google Assistant, Teachable Machine

Tools: React, HTML, CSS, Javascript, Ionic, Tensorflow JS

Libraries: pl5 and ml5 (React)

## Challenges we ran into

Serving static HTML on React, and refining the UI to maintain the accessibility of the app, such as ensuring that appearance is uniform across all platforms.

## Accomplishments that we're proud of

The usability, accessibility (web, android, iOS), and cost-effectiveness (no expensive technologies or proprietary hardware used) of the app.

## What we learned

JavaScript, how to convert a React app into a Progressive Web app, how to implement Google Assistant and Alexa.

## What's next for Protect-21

Implementing a functionality for saving multiple key locations.
## Inspiration

The majority of cleaning products in the United States contain harmful chemicals. Although many products pass EPA regulations, it is well known that many products still contain chemicals that can cause rashes, asthma, allergic reactions, and even cancer. It is important that the public has easy access to information of the chemicals that may be harmful to them as well as the environment.

## What it does

Our app allows users to scan a product's ingredient label and retrieves information on which ingredients to avoid for the better of the environment as well as their own health.

## How we built it

We used Xcode and Swift to design the iOS app. We then used Vision for iOS to detect text based off a still image. We used a Python scrapper to collect data from ewg.org, providing the product's ingredients as well as the side affects of certain harmful additives.

## Challenges we ran into

We had very limited experience for the idea that we have in developing an iOS app but, we wanted to challenge ourselves. The challenges the front end had were incorporating the camera feature and the text detector into a single app. As well as trying to navigate the changes between the newer version of Swift 11 from older versions. For our backend members, they had difficulties incorporating databases from Microsoft/Google, but ended up using JSON.

## Accomplishments that we're proud of

We are extremely proud of pushing ourselves to do something we haven't done before. Initially, we had some doubt in our project because of how difficult it was. But, as a team we were able to help each other along the way. We're very proud of creating a single app that can do both a camera feature and Optical Character Recognition because as we found out, it's very complicated and error prone. Additionally, for data scrapping, even though the HTML code was not consistent we managed to successfully scrap the necessary data by taking all corner cases into consideration with 100% success rate from more than three thousand HTML files and we are very proud of it.

## What we learned

Our teammates working in the front end learned how to use Xcode and Swift in under 24 hours. Our backend team members learned how to scrap the data from a website for the first time as well. Together, we learned how to alter our original expectations of our final product based on time constraint.

## What's next for Ingredient Label Scanner

Currently our project is specific to cleaning products, however in the future we would like to incorporate other products such as cosmetics, hair care, skin care, medicines, and food products. Additionally, we hope to alter the list of ingredients in a more visual way so that users can clearly understand which ingredients are more dangerous than others.
## Inspiration

Our goal from the beginning was to solve a **real world problem**, but we didn't know where to begin. We started by asking our mentors for advice. It was during a conversation with an RBC mentor that we learned about the problem of credit card fraud. It looked like a problem we could solve using an adaptive machine learning algorithm, and that was our motivation to come up with an analytics product like Panthera.

## What it does

Panthera is a command line application. It has no front end due to this it is incredibly well suited for developers looking to move quickly. This is how it works: You provide Panthera with a dataset of all credit card transaction during a time period in .csv format. Panthera then analyses the data pre-training, and uses it's insight from this to select the best algorithm for the job. This makes Panthera highly adaptive to the type of data set.

After a few minutes of training, Panthera runs it's machine learning logic on a subset of the transactions, and tells the developer how accurate it was in identifying fraudulent transactions (along with a few other stats).

As of writing, and using the sample data set of 284,807 transactions from Europe containing 492 fraudulent transactions, Panthera was able to successfully flag fraudulent the transactions 91% of the time.

## How to use it

Simply open up Terminal.app, navigating to the directory where `runme.py` + the dataset is stored and typing in

`python runme.py`

## How we built it

We built Panthera considering the future of machine learning. Python is incredibly popular for machine learning, has a lot of efficient and useful machine learning libraries, and has a promising future. For these reasons, Panthera was written using Python. We spent a lot of time honing in on the right subset of algorithms for the job, through multiple iterations.

## Challenges we ran into

Selecting the right algorithms for different data sets was the biggest challenge. We needed to identify one that could work fast/efficiently and accurately with large data sets. We decided to go with bunch of 3 algorithms, of which were linear, and 1 was non-linear to encompass as many possible transaction databases as possible.

## Accomplishments that we're proud of

Being able to learn the crux of machine learning in 12 hours, and then applying it into a single application is something that we're incredibly proud of.

## What we learned

Machine Learning is really a simple process. The challenge with Machine Learning is to draw the right correlations from the data, perform statistical analysis of the outcomes (realizing 91% accuracy is not a great number when dealing with more than 100M transactions).

Not to mention, learning new things is always **fun**.

## What's next for Panthera?

We really want to go deeper into making Panthera more efficient. Answering questions like, how can we use Deep Learning, multi layered systems, or Recurrent Neural Networks to make Panthera more accurate.