Millennials having money problems

What it does

We try to estimate your dream income based on how much you spend over your means each day. We look at various features including gender, age, education, and also your daily 'extra' transactions to show you your estimated income, in order to show you whether you're living out of you means or not,

How we built it

Jupyter notebook platform, using sk-learn library for linear regression

Challenges we ran into

We had to strike down our initial project halfway through the hackathon because of unforeseen complications. And then, we had to learn about the basics of machine learning and data science by attending the workshops etc to be able to take on this project.

Accomplishments that we're proud of

After initially struggling with finding feasible ideas, and then having to pivot at a crucial time (night before judging), we are proud of finishing the project. Also, we're proud of being able to learn so much about ML and data science and actually put it to work within 12 hours.

What we learned

-Working with APIs (TD API mainly for testing) -The basics of machine learning and data science -More about the sk-learn library for machine learning

What's next for Means Checker

Find a bigger better database for training, and possibly look at other features such as location and dependants.

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