International Football Data Analytics (1872–2024)

A professional data analysis project exploring over 150 years of international football results. This project utilizes Python and Jupyter Notebooks to derive insights from the Kaggle "International Football Results from 1872 to 2024" dataset.

📊 Project Overview

This repository contains a modular and professional approach to data exploration and visualization. The logic is separated into a core analytics module, ensuring maintainability and scalability.

Key Features

• Data Preprocessing: Handling datetime conversions and result categorization.
• Statistical Analysis: Deep dive into goal trends and historical team performance.
• Visualization: High-resolution, professional charts using Seaborn and Matplotlib.
• Home Advantage Study: Statistical evaluation of match outcomes based on location.

📂 Project Structure

├── data/
│   └── results.csv           # Raw dataset
├── football_analytics.py     # Core analytics engine (Modular Logic)
├── football_analysis.ipynb   # Interactive analysis report
├── requirements.txt          # Project dependencies
└── README.md                 # Project documentation

🚀 Getting Started

Prerequisites

Ensure you have Python 3.8+ installed.

Installation

1. Clone the repository or download the files.
2. Install the required dependencies:

   pip install -r requirements.txt

Running the Analysis

Open the Jupyter Notebook to view the full report:

jupyter notebook football_analysis.ipynb

📈 Insights Preview

• Analysis of goal distributions over a century of play.
• Identification of the top 10 most successful international teams.
• Verification of "Home Advantage" through match outcome percentages.