Injuries pose a significant threat to athletes, often leading to career-ending consequences. Accurate injury prediction can empower athletes and coaching staff to make informed decisions about training loads and recovery strategies, thereby minimizing the risk of injuries.
This project focuses on developing a machine learning-based classification model to predict the likelihood of injuries. By analyzing key factors such as player demographics, training intensity, and historical injury data, the model aims to provide actionable insights for injury prevention.
The dataset used in this project contains 1,000 player records, each characterized by the following features:
- Player Age: 🧑 Age of the player (in years).
- Player Weight: ⚖️ Weight of the player (in kilograms), normally distributed with a mean of 75 kg and a standard deviation of 10 kg.
- Player Height: 📏 Height of the player (in centimeters), normally distributed with a mean of 180 cm and a standard deviation of 10 cm.
- Previous Injuries: 💢 Binary indicator (0 or 1) showing whether the player has had previous injuries (1) or not (0).
- Training Intensity: 🔥 A value between 0 and 1 representing the intensity of the player's training regimen.
- Recovery Time: ⏳ The number of days required for the player to recover from an injury, ranging from 1 to 6 days.
- Likelihood of Injury: 🚑 The target variable, a binary indicator (0 or 1) predicting the likelihood of the player experiencing an injury (1) or not (0).
The main goal of this project is to compare different machine learning models to accurately predict sports-related injuries. By analyzing factors such as age, weight, height, previous injuries, training intensity, and recovery time, the model aims to identify injury patterns, enabling better decision-making to reduce injury risks.
- Data Preprocessing: 🧹 Cleaning and preparing the dataset for training.
- Model Selection: 🧠 Evaluating and comparing various machine learning algorithms.
- Model Training: 🎓 Training models to predict injury likelihood.
- Evaluation: 📈 Assessing model performance to determine the best approach.
This project aims to build a robust injury prediction tool, aiding in the health and longevity of athletes by fostering data-driven decisions. The insights from this model could be instrumental in minimizing injury risks and optimizing player performance.