FIFA 22 : Exploratory Data Analysis
This analysis includes importing libraires and exploring the data. This includes data manipulation and cleaning and data analysis.

Import pandas, a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language

Import numpy, NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

Import random, Returns a non-negative Python integer with k random bits. 

Import plotly, a Python graphing library which makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.

Import Seaborn, a Python data visualization library based on matplotlib.

Import Matplotlib, Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

Machine Learning

import Sklearn, a Machine Learning tool in Python · Simple and efficient tools for predictive data analysis · Accessible to everybody, and reusable in various contexts. Linear regression uses the relationship between the data-points to draw a straight line through all them. This line can be used to predict future values. DecisionTreeClassifier is a class capable of performing multi-class classification on a dataset.

import pandas as pd 
import numpy as np
import random as rnd
import plotly.express as px

# visualization
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

# machine learning
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeClassifier

df = pd.read_csv('players_22.csv')