Inspiration
We really felt that as young adults, we realized that having a realistic approach to how much money we can make and choose a career catered to the lifestyle we would want to live.
What it does and how we built it
The python backend webscrapes salary data from job bank of Canada and finds the salaries corresponding to the desired profession and desired province. The front end has a main page where you submit your desired profession and province and it will redirect you to a page with salary information pre federal tax and post federal tax. We calculated federal tax using javascript with values from a website( not webscraped). We used flask to interconnect front end and back end.
Challenges we ran into
The hardest part we encountered was completing our backend with Flask (so that we can extract the inputs from our website and use it with our python script) and figuring out how to web scrap the salary data.
Accomplishments that we're proud of
None of us were familiar with either Flask or web scraping. During this hackathon we learned a lot about these concepts. Also considering that we didn't have the full 24hrs to work on this project due to time conflicts, we think we did quiet well.
What we learned
We learned a lot about Flask and creating a bridge between html and python. This was the most difficult part because no one in the team really knew how to use Flask, but after watching some videos, asking mentors for help, and reading on some documentation, we were able to create a functional website that web scrapes and returns the minimum, median, and maximum salary. We also learned a lot about creating forms and accessing the values of the user input for our data processing.
What's next for Future Financial Earnings
There is a lot of room for this project to grow. We can incorporate different taxes based on provinces and can include different lifestyle choices they can make like the car they can buy and the house and mortgage they can pay.
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