This project implements a basic K-Nearest Neighbors (KNN) algorithm for classifying Pokemon types using their stats. The implementation is done from scratch without using any machine learning libraries.

The project uses the Pokemon database, which is publicly available and will be uploaded to the project's GitHub repository.

• Python 3.x

1. Clone the repository
2. Run the main script: python knn_pokemon.py

• knn_pokemon.py: Main script containing the KNN implementation and analysis
• pokemon_database.csv: Dataset file
• README.md: This file

• No machine learning libraries are used in the KNN implementation
• The project explores the relationship between the 'k' parameter and overfitting