This project involves setting up a database to store FIFA player data, importing the data into the database, and performing data analysis to generate visualizations and statistics. The project uses Python, Pandas, SQLAlchemy, Matplotlib, and Seaborn to achieve these tasks.

• Python 3.x
• MySQL database server
• Required Python packages: pandas, sqlalchemy, matplotlib, seaborn, pymysql

1. Clone the Repository

   git clone https://github.com/your-username/fifa-player-data-analysis.git
   cd fifa-player-data-analysis

2. Install Required Packages

   pip install pandas sqlalchemy matplotlib seaborn pymysql

3. Download CSV Data Download the CSV files from the Kaggle FIFA 23 Complete Player Dataset:

   • male_players.csv
   • male_teams.csv

   Place these files in the project directory.

4. Database Setup Ensure MySQL is installed and running. Create a database for the project:

   CREATE DATABASE fifa;

5. Update Database URI Update the DB_URI in both data_import.py and statistics.py scripts to match your database credentials:

   DB_URI = 'mysql+pymysql:https://fifa:fifa@localhost:3306/fifa'

1. Run the Data Import Script

   python data_import.py

   This script will:

   • Connect to the MySQL database.
   • Create the necessary tables (teams, nationalities, positions, players).
   • Import data from male_players.csv and male_teams.csv into these tables.

1. Run the Data Analysis Script

   python statistics.py

   This script will:

   • Connect to the MySQL database.
   • Fetch data from the database.
   • Clean the data.
   • Generate and save visualizations:
     • Distribution of Player Overall Ratings
     • Average Overall Rating by Player Position
     • Top 10 Teams by Average Player Rating
     • Top 20 Nationalities by Player Count

The generated plots will be saved as PNG files in the project directory.