## Inspiration

We've always wanted to tackle a project that involved processing huge amounts of data. We also wanted to make a project that can help students at McGill explore and audit classes so that they can discover their interests.

## What it does

The McGill map is a map that allows students to see all current courses taking place on the current day and time. Additionally, students can select their desired day and time and find the courses taking place.

## How we built it

We did all the data analysis and manipulation using python and use a mySQL database to store it. We then used flask as a backend and integrated that with our frontend that we made with Svelte using TypeScript, HTML and CSS as well as the Google Maps Javascript API.

## Challenges we ran into

The hardest part of this project was the huge amount of data we needed to collect and organize. Getting the data analysis working was definitely a challenge. Additionally, we are not used to frontend so getting everything set up took longer than expected.

## Accomplishments that we're proud of

We're proud that we have a very usable project that is simple and easy to use by the end-user.

## What we learned

We gained more data analysis skills as well as expanded our frontend knowledge as well as familiarity with the Google Cloud Platform.

## What's next for The McGill Map

We think this project could be very useful to McGill students as well as administrators who would like an easier way to visualize activities taking place at McGill. We would love to take this project further and try to add more visualizations to the project such as traffic flow analysis using AI.
## Inspiration

A common problem faced by college students and working professionals alike is finding a compatible roommate to live with. We wanted to create a solution to make this an easier process.

## What it does

Roomify allows users to create profiles and returns matched profiles based on categories such as location, age, and optionally, gender.

## How we built it

This web application was built using Java and Netbeans. The front end was built using HTML. We used the Apache Tomcat server.

## Challenges we ran into

Starting off since neither of us have much experience with Full Stack development, we were a little lost with where to begin for creating a web application. Using our prior knowledge of Java and HTML, we decided to use NetBeans. This entire project was a lot of learning as we were implementing - how servers work, the differences between .xml, html files, and how to design the interaction between servlets. Understand the Java framework that was used along with the server interaction, took the most amount of time. Having prior knowledge of the programming languages came in *clutch* the most in regard to confidence, but the learning an entirely new framework and interface on-the-go took the most brain power.

## What's next for Roomify?

Eventually, we would like to implement a server or database for saving all of our user’s profiles and allow the end user to scroll through multiple matches rather than have only one match. Due to time constraints and lack of previous experience, we had to manually create most of the existing profiles. Moreover, we plan to implement a login/authentication mechanism for the application, and a built-in messenger for users to be able to communicate with each other solely through our app.
## Inspiration

As recent or soon to be graduates, we personally understand the desire to relocate and expand our world views. There is so much potential out there, but it's hard to know what city is best as we all have unique needs and wants.

## What it does

By gathering aspects that students care about when researching a city, we visualize the data based on selected preferences and suggest potential cities. Clicking on a city shows more information about that city and how it compares to others.

## How I built it

We initially narrowed our focus to a set of users: recently graduated students. Then, we discussed several user journeys and sought out specific pain points. We conducted some research to find out what type of criterias people look into when deciding where to move, and then found open datasets from statscan and other online sources to support these criteria.

We pulled the 2016 Canadian Census Data information on the biggest cities in Canada. We sorted this data into specific categories, and compiled static JSON files of the cities. We then fed this information into our web app powered by React where we visualized it using Mapbox and different graphing techniques.

## Challenges I ran into

Going from a well designed static prototype to an implemented version is a big jump as the data had to be manipulated to fit the visualization library we used. The Stats Canada data was also unreliable and oddly formatted, leading to a lot of difficulties.

## Accomplishments that I'm proud of

We managed to design a data visualization that makes use of multiple datasets and combined them in a cohesive way that helps students make an informed decision.

## What I learned

We learned that the quality of the data sets was not only dependent on the source it came from, but also the richness of the data in providing value in visualization. In certain fields the data was especially shallow which made it difficult to draw any useful visualizations.

## What's next for LeaveTheNest

We would love to explore how students can learn from our visualization and where to expand next. Right now we focused on Canadian data, but the next step would be to include American cities and beyond. We would also love to explore more intricate data visualizations that can dig deeper into the data and provide more value.