Sports-Performance-Analyzer

Dataset: https://www.kaggle.com/datasets/ziya07/athlete-injury-and-performance-dataset/data

Overview

This project analyzes how various physical, training, and injury-related factors influence athlete performance levels. It uses multiple machine learning models to predict performance outcomes and provides insights through exploratory data analysis (EDA) — including gender-based performance comparison and feature importance interpretation.

The dataset is sourced from Kaggle — Athlete Injury and Performance Dataset.

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Objectives

• Understand how training and injury attributes affect athletic performance.
• Compare the impact of gender differences on performance outcomes.
• Train and evaluate multiple ML models to classify athlete performance.
• Identify the most important factors influencing performance using feature importance.

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Dataset Description

The dataset contains information about:

• Athlete demographics (e.g., Age, Gender, BMI)
• Training metrics (e.g., Hours per Week, Training Intensity)
• Injury records (e.g., Injury Type, Injury Duration)
• Performance Level (target variable — categorical or numeric)

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Methodology

1. Data Preprocessing

• Handle missing values (median for numeric, mode for categorical).
• Encode categorical variables using LabelEncoder.
• Scale features using StandardScaler.

2. Exploratory Data Analysis (EDA)

• Visualize correlations between training, injury, and performance features.
• Analyze gender distribution and performance by gender using boxplots and t-tests.
• Identify significant statistical differences between male and female performance.

3. Model Training

A variety of classification models are compared:

Model	Description
Logistic Regression	Baseline linear model
SVM	Support Vector Machine
Random Forest	Ensemble of decision trees
Gradient Boosting	Boosted tree-based model
KNN	k-Nearest Neighbors
Naive Bayes	Probabilistic model
Decision Tree	Single tree classifier
AdaBoost	Adaptive boosting classifier
Extra Trees	Randomized ensemble
XGBoost	Gradient boosting with regularization

4. Model Evaluation

Each model is evaluated on:

• Accuracy
• ROC-AUC score
• Classification report (precision, recall, F1-score)

Results are visualized through a model comparison bar chart.

5. Feature Importance

For tree-based models (Random Forest, XGBoost, etc.), feature importance is plotted to identify top predictors of performance.

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Gender-Based Analysis

• Gender distribution visualization
• Performance comparison (Male vs Female)
• Statistical testing (t-test) for performance difference
• Gender vs Performance Level heatmaps and plots

This helps determine if gender plays a significant role in performance or injury-related outcomes.

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Outputs

• Correlation heatmap
• Gender-based boxplots and countplots
• Model comparison bar chart (accuracy and AUC)
• Feature importance plot

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Results Summary

• Models are ranked by accuracy and ROC-AUC score.
• Insights on which attributes (e.g., training load, injury duration, gender) most strongly affect performance.
• Statistical evidence of whether gender differences in performance are significant.