⚽ Football Player Statistics Analysis & Prediction

📌 Overview

This project explores football player statistics using data analysis and machine learning to identify performance trends and predict player attributes. The dataset contains extensive player attributes, including position, skill ratings, passing accuracy, and defensive capabilities, sourced from FIFA or similar football databases.

By leveraging Python, Pandas, Matplotlib, and Scikit-learn, we analyze patterns, compare players across leagues, and build predictive models for estimating player performance.

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🚀 Key Features

• 📊 Exploratory Data Analysis (EDA)

  • Analyzed player attributes and distributions.
  • Identified key features influencing player performance.
  • Visualized trends using Matplotlib & Seaborn.

• 🔍 Machine Learning for Prediction

  • Trained Regression & Classification models to predict player ratings.
  • Applied Random Forest, Linear Regression, and Decision Trees.
  • Evaluated models using R², RMSE, and feature importance analysis.

• 📈 Data Visualization

  • Built interactive visualizations to compare players.
  • Used correlation heatmaps, histograms, and boxplots.

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🔧 Tech Stack

• Programming Language: Python
• Data Processing: Pandas, NumPy
• Visualization: Matplotlib, Seaborn
• Machine Learning: Scikit-learn
• Jupyter Notebook for interactive analysis

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📈 Key Insights

• Most important attributes for high-rated players:

  Passing Accuracy, Dribbling, and Physical Strength significantly impact overall ratings.

• Positional Analysis:

  Midfielders show higher passing accuracy, while strikers excel in finishing.

• Machine Learning Results:

  Random Forest Regression provided the best accuracy for rating predictions.

• Statistical Distribution:

  Higher-rated players tend to have more balanced attributes across skills.