🏆 World Cup Matches — Data Analysis & Visualization

A Python project performing exploratory data analysis (EDA) and visualization on historical FIFA World Cup match data using Pandas, Seaborn, and Matplotlib.

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📁 Repository Structure

world_cup_matches_data_analysis/
├── worldcup.py             # Main analysis script
├── WorldCupMatches.csv     # Dataset
└── README.md

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📊 Dataset

WorldCupMatches.csv — Match-level data from FIFA World Cup tournaments, including home/away teams, goals scored, year, stage, and more.

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🔧 Requirements

• Python 3.x
• pandas
• seaborn
• matplotlib

Install dependencies:

pip install pandas seaborn matplotlib

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🚀 Usage

python worldcup.py

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📝 Features

1. Data Analysis

Step	Description
Shape	Prints number of rows and columns using df.shape
Column Names	Lists all column names in the DataFrame
Slicing	Displays rows 10–20 using df.iloc[10:21]
Summary Statistics	Uses df.describe() for min, max, mean, std, etc.
Min / Max	Finds minimum and maximum Home Team Goals

2. Data Cleaning

• Missing Value Detection — df.isnull().sum() counts nulls per column
• Imputation — Fills missing numerical values using column mean/median via df.fillna()
• Correlation Matrix — Computes pairwise correlations across all numeric columns using df.corr()

3. Data Visualization

Plot	Library	Description
Pairplot	Seaborn	Pairwise relationships across all numeric columns
Boxplot	Seaborn	Distribution and outlier detection
Heatmap	Seaborn	Correlation matrix with coolwarm colormap
Bar Plot	Seaborn	Home Team Goals vs Away Team Goals
Histogram	Matplotlib	Distribution of Away Team Goals
Distribution Plot	Seaborn	KDE + histogram for Home Team Goals
Scatter Plot	Seaborn	Home vs Away goals with individual data points

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👤 Author

Rahul Biswas GitHub: @RahulBiswas224

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📄 License

This project is open source and available for educational purposes.