Scrapping data from the transferMarket website about Summer Football 2022 markert transfer

Data scraped from Market Transfer website in below images show the data that has scraped for each player.

for example Phili Foden mancity player

• Frist webpage has his name, age, market value and other.
• Second webpage has some insights about progress with team.
• Third webpage has some insights about progress with National team.

Dataset Descrption

Data contains 28401 players each one has 19 feature represents below.

Feature Name	Type	Description
age	Numerical	Player age at this season
position	Categorical	player position could be goolkeeper, defence, midfield or attack
country	Categorical	Player country
apperance	Numerical	No. Played matches
goals	Numerical	No. Scored goals
assists	Numerical	No. Assists
yellow_card	Numerical	No. Yellow card
2nd_ycard	Numerical	No. 2nd yellow card
red_card	Numerical	No. Red card
min	Numerical	No. Played minutes
National_Team	Binary	Refer to has play with national team before or not 1 play and 0 doesn't
apperance_NT	Numerical	No. Played matches with national team
goals_NT	Numerical	No. Scored goals with national team
assists_NT	Numerical	No. Assists with national team
yellow_card_NT	Numerical	No. Yellow card with national team
2nd_ycard_NT	Numerical	No. 2nd yellow card with national team
red_card_NT	Numerical	No. Red card with national team
min_NT	Numerical	No. Played minutes with national team
cost	Numerical	Transfer cost

libraries used

Regex used to extract data from the website using some libraries:

1. selenium
2. beautifulsoup
3. requests

Machine learning algorithms

Model Name	Train accuracy %	Test accuracy %
Simple Linear Regression	75.07	71.97
XGBoost Algorithm	99.15	98.99

Model Name	Train MSE	Test MSE
Neural Network	2.6162e-05	2.0937e-05

Graph show losses during training and validation