PlayerMatch

Inspiration

Initially, we wanted to make a data analysis between the month of the birthday and the eur_value of each player. They seems unrelated to each other, but there could be some interesting results. While we were doing that, we realized why don't we take in some possible, but not definite factors, and make an app to predict which player will be given the input of birthday, height, weight and age. It will be fun to see which professional I will be, if put in my parameters.

What it does

It is a little predictive app for fun, which takes in whatever input you provide, and give back the most possible professional player that you can become.

How I built it

We did the data analysis using python, where we get all the formulas from. Then we used javascript to import the data and compare the result with the original data to get the most possible person that matches to, given the parameters.

Challenges I ran into

We have never used Python before, it is hard to import the correct libraries and use them to do the regression. First time, when we were using Javascript, the loading speed is slow, and we need a solution to increase the loading speed.

Accomplishments that I'm proud of

We created a moderately accurate model to analyze the relationship between birthdate, height, weight, and age. We did the regression in a language that we have never used before.

What I learned

We learned the importance of brainstorming. And we should not be afraid of failure, be prepared to learn new things at any time.

What's next for PlayerMatch

Built With

• html
• jupyter-notebook