Athlete Selection – KNN Model

This project explores athlete data to help with selection decisions using a K-Nearest Neighbors (KNN) model.
The goal is to identify the nearest neighbors of a given athlete based on performance features.

🧠 What This Project Covers

• Data loading and preprocessing
• Applying KNN with sklearn
• Identifying top 3 nearest athletes
• Understanding model performance and accuracy

📁 Dataset

The dataset was sourced from Kaggle and contains physical performance metrics for athletes.

🛠️ Tools & Libraries

• Python
• Pandas
• Scikit-learn
• NumPy
• Jupyter Notebooks (Kaggle)

🏷️ Tags / Labels

KNN Machine Learning Classification Athlete Data Sklearn Beginner Project Sports Analytics

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🚀 How to Run

1. Clone the repo
2. Open the notebook in Jupyter or VS Code
3. Make sure required libraries are installed
4. Run all cells and explore the results!