This project provides an in-depth analysis of FIFA World Cup data using Python. It covers key aspects of the matches history, performance trends, and standout insights. Whether you're a football enthusiast or a data analytics fan, this project highlights the intersection of sports and data science.
https://www.kaggle.com/datasets/piterfm/fifa-football-world-cup
The dataset contains information about all matches and results that took place in Football/Soccer FIFA World Cups.
fifa_ranking.csv - teams ranking before Qatar 2022 World Cup.
matches.csv - table contains all match results from 1930 to 2022.
world_cup.csv - table includes basic information about each World Cu
- Data Cleaning and Preprocessing: Handling raw datasets for analysis-ready insights.
- Exploratory Data Analysis (EDA): Visualizations and statistical summaries.
- Historical Trends: Analysis of tournament results, team performances, and player statistics.
- Insights and Patterns: Observations about winners, goal patterns, and other noteworthy trends
- Python: For data analysis and visualization.
- Jupyter Notebook: Interactive environment for code and analysis. Libraries:
- pandas for data manipulation.
- matplotlib and seaborn for visualizations.
This project addresses four central questions about the FIFA World Cup, uncovering valuable insights:
- Which teams have historically performed the best in the World Cup?
- How has the average number of goals per game changed over the years?
- What are the win ratios of notable teams when playing on their home ground?
- what are Number of goals scored by the top 5 players?