Sport player analysis using machine learning

This project, titled "Sport Player Analysis Using Machine Learning," focuses on utilizing advanced machine learning techniques to evaluate football players' performance. With the help of a dataset containing detailed statistics on over 17,000 football players, the system aims to predict player ratings based on their skills, attributes, and other performance metrics. The dataset includes information on various factors such as player age, nationality, market value, preferred foot, and ratings for attributes like passing, dribbling, shooting, and more. To achieve this, machine learning algorithms like Random Forest, XGBoost, and Support Vector Machines (SVM) are used, alongside a hybrid model to improve prediction accuracy.The model is designed to provide a clear assessment of players, allowing users to predict a player's potential and overall rating based on their input data. The application is built using HTML, CSS, and JavaScript for the front-end, and Python with Flask for the back-end. It also provides a user-friendly interface that allows users to register, log in, and access the prediction or classification features. The system can predict future performance, which can be valuable for analysts, coaches, and football enthusiasts alike.The project makes use of feature engineering, data cleaning, and model training techniques to ensure accurate and reliable predictions. The inclusion of hybrid models helps in enhancing the prediction quality by combining the strengths of different algorithms. This research-based system also holds potential for further improvement, such as incorporating more data or exploring other machine learning models.

Keywords: Machine Learning, Football, Player Analysis, Random Forest, XGBoost, Support Vector Machine, Data Processing, Model Prediction, Hybrid Model, Flask.