This project performs a concise and insightful analysis on a football player dataset using Python. The focus is on player age statistics and league distribution using data visualization techniques.
The dataset includes basic details about football players, such as:
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Player: Name of the player
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Age: Age of the player
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National Team: Player's country
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Club: Club name
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League: League where the player plays
1️⃣ Overall Age Distribution A bar plot showing the age of all players to visualize general trends.
2️⃣ Top 10 Youngest Players Displays the youngest 10 players in ascending order of age.
3️⃣ Top 10 Oldest Players Highlights the oldest 10 players based on their age.
4️⃣ Player Count by League A horizontal count plot showing the number of players in each league.
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Fixed incorrect values and missing data (e.g., wrong age, league, National Team or Club).
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Converted age column to numeric.
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Sorted and filtered to get top 10 youngest/oldest players.
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Standardized column names and data formatting.
- python
- pandas
- numpy
- matplotlib
- seaborn
Found age patterns among professional footballers.
Identified extreme age players (youngest and oldest).
Compared league-level player distribution using visual methods.
Syed Danish Ahmed
Aspiring Data Scientist | Computer Engineering Student
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