This project focuses on analyzing and visualizing a dataset of football players using Python libraries such as pandas, matplotlib, and seaborn. The goal is to uncover insights into player statistics, including their preferred foot, skill moves, reputation, and performance metrics.

🔍 Key Analyses Performed: Distribution Analysis:

Preferred foot (Left vs. Right). Weak foot ratings of players. Age distribution of players. Performance Metrics:

Visualization of international reputation. Special scores of players. Position-Based Insights:

Number of players by position. Work rate of players categorized by preferred foot. Country-Wise Analysis:

Total number of countries and their representation. Top 10 countries with the most players. Age, overall score, and potential score distributions for players from top countries. Club Information:

Total number of clubs. List of club names. Top Performers:

10 best players overall. 10 oldest players in the dataset. 📊 Visualization Techniques: Bar plots and histograms using matplotlib and seaborn. Box plots for comparing scores across top countries. Scatter plots for analyzing assists vs. goals. ⚙️ Technologies Used: Python 🐍 Pandas 📊 Matplotlib 📈 Seaborn 🎨 NumPy ➗