FIFA Data Analysis

Description

The FIFA Data Analysis Project is a data-driven approach to analyzing player performance, potential, and overall ratings in FIFA datasets. Using exploratory data analysis (EDA), visualizations, and machine learning models, this project helps uncover key insights into player attributes, club trends, and predictive analytics.

🔹 Who Can Use This?
✅ Football Enthusiasts & Analysts – Explore FIFA player statistics, clubs, and attributes.
✅ Data Scientists & ML Practitioners – Learn how to clean, analyze, and model sports data.
✅ Machine Learning Enthusiasts – Build and fine-tune predictive models for player ratings.
✅ Researchers & Students – Use this project as a case study for sports analytics.

Key Features:

✔ Comprehensive FIFA Player Data Analysis – Examine attributes, clubs, nationalities, and player trends.
✔ Advanced Data Cleaning & Preprocessing – Handles missing values, outliers, and feature engineering.
✔ Exploratory Data Analysis (EDA) & Visualizations – Includes heatmaps, jointplots, pairplots, and regression plots.
✔ Predictive Modeling – Uses Linear Regression and Permutation Importance for player rating predictions.
✔ Machine Learning Integration – Train and test ML models to predict player overall ratings.
✔ Fully Customizable & Extendable – Modify datasets, add new features, and improve prediction accuracy.