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data_analysis_fifa_soccer's Introduction

Data Analysis

A Look at FIFA Soccer Players Attributes

This project uses Python with pandas, SQL and principal component analysis. The database was downloaded from below: https://www.kaggle.com/hugomathien/soccer/data

https://www.kaggle.com/dimarudov/data-analysis-using-sql/data This is the ultimate Soccer database for data analysis and machine learning.

 European Soccer Database (from Kaggle.com)
 +25,000 matches
 +10,000 players
 11 European Countries with their lead championship
 Seasons 2008 to 2016
 Players and Teams' attributes* sourced from EA Sports' FIFA video game series

Research Questions:

  1. Which player’s attribute contributes most to player’s overall rating

  2. What attributes set players apart

Findings:

  1. Strong positive linear correlation between
       a. Overall Rating and Potential (Coeff. = 0.7840)
       b. Overall Rating and Reactions (Coeff. = 0.7248)
  2. Defending and goalkeeping attributes as a whole set players into subgroups
       a. Total Defending Score: marking, standing tackle and sliding tackle
       b. Total Goalkeeping Score: diving, handling, kicking, positioning and reflexes

PowerPoint: https://github.com/Saukha/Data_Analysis_FIFA_Soccer/blob/master/majorproject_fifa_players_powerpoint_v5.pdf

Final Report: https://saukha.weebly.com/uploads/1/1/8/0/118025626/major_project_report_05212018.pdf

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