Air Quality Prediction for Outdoor Activities using K-Means Clustering

This project applies K-Means clustering to predict air quality and determine the best times for outdoor activities based on historical data. By clustering data into different pollution levels, the system recommends the most suitable times for outdoor events, helping people avoid exposure to unhealthy air conditions.