FIFA-Player-Performance-ML

FIFA Player Performance Prediction — Machine Learning ProjectProject Overview:An end-to-end Machine Learning and Data Analytics project aimed at analyzing soccer players' metrics and predicting their overall performance and market values based on individual attributes (e.g., pace, shooting, passing, positioning, and physical stats).Key Highlights:Exploratory Data Analysis (EDA) & Preprocessing: Cleaned the raw player dataset, handled missing values, encoded categorical features, and applied feature scaling.Model Development & Evaluation: Implemented and evaluated various machine learning models (e.g., Linear Regression, Random Forest, XGBoost) to predict performance metrics, measuring accuracy using standard evaluation benchmarks (RMSE, MAE, $R^2$).Feature Importance Analysis: Identified the most influential technical and physical attributes driving overall player ratings and potential.Tech Stack:Language & Libraries: Python, Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn