Mini-projects on data science using python:

FIFA: Data analysis on FIFA datasets

(a work in progress)

Datasets can be obtained from:

https://www.kaggle.com/stefanoleone992/fifa-20-complete-player-dataset

Prerequistes:

Seaborn needs to be installed to recreate the visuals.

pip install seaborn

Requirements:

The project uses the standard data science libraries.

import pandas
import numpy
import matplotlib.pyplot

Results

Foot preference for players.

Some the best ever players are left-footed. They sure are rare!

Here is a look at the distribution of the various player positions

Interesting to see the defensive positions take the lead in this distribution!

Now let's tke a look at FIFA ratings for players!

Shows how majority of players of players lie around 65 and 70, for overall and potential ratings respectively. The far right is where the absolute ballers reside - some of the most elite atheletes in the world surely

A footballers (weekly) earnings ? - Madness 🤯

Footballers are some of the highest paid individuals on the planet. Even with weekly earnings that are only a dream to most, the sheer difference between the wages and market value of the best and the rest of them is astounding!

Yet another segment where Messi and Ronaldo stand clear of everyone else.:star_struck:

(If only Ronaldo wasn't as old as he is - he would've been right up there with Messi on the wages and value charts!. Not that his performances are affected in any way. Truly a machine 🦾)

Finally we take a look at the different countries that are represented in FIFA

(Or at least the Top 80 Nations)

Do you see your country in there?

shiva-adith/Data_science_projects