🏅 Athlete Performance Prediction

Predict athletic performance using biometric and training metrics with the power of machine learning.

📌 Project Overview

This project utilizes a dataset of athlete metrics (e.g., heart rate, stamina, training hours) to predict performance levels. It offers a streamlined pipeline—from data processing to model prediction—and a user-friendly web interface powered by Streamlit.

🚀 Features

• 📊 Exploratory Data Analysis (EDA)
• 🧠 Model Training using scikit-learn
• 💾 Pipeline Serialization (pipe.pkl)
• 🌐 Streamlit UI (app.py) for real‑time predictions
• 📈 Visualizations and insights included in Jupyter Notebooks

🗂️ Project Structure

Athlete-Performance-Prediction/
├── app.py                      # Streamlit application
├── pipe.pkl                    # Serialized machine learning pipeline
├── train.csv                   # Training dataset
├── requirements.txt            # Project dependencies
├── Athlete-Performance-Prediction.ipynb # Main notebook for data exploration & model training
├── Athlete_performance_prediction.ipynb # Alternate/preliminary notebook
└── README.md                   # Project documentation

🧪 Tech Stack

• Python
• Pandas, NumPy
• scikit-learn
• Streamlit
• Jupyter Notebook
• Matplotlib / Seaborn

📦 Setup Instructions

1. Clone the repository

   git clone https://github.com/Stu-ops/Athlete-Performance-Prediction.git
   cd Athlete-Performance-Prediction
   

2. Create a virtual environment (optional but recommended)

   python -m venv venv
   source venv/bin/activate  # On Windows use: venv\Scripts\activate
   

3. Install dependencies

   pip install -r requirements.txt
   

4. Run the Streamlit app

   streamlit run app.py
   

🔍 Model Details

• Features Used: heart_rate, stamina, training_hours, and other biometric stats.
• Target: Athlete performance label (categorical or numeric rating).
• Model: Regression or classification model trained using scikit-learn.
• Pipeline: Serialized using joblib and saved as pipe.pkl for reuse in the Streamlit app.

📈 Example Use Case

A sports analyst inputs athlete metrics into the UI and receives an instant prediction of their performance potential. This tool can assist in:

• Training optimization
• Talent scouting
• Injury prevention analytics

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Generated on May 6, 2025