This project analyzes the Vertebral Column Dataset to classify orthopedic patients as either normal or abnormal based on six biomechanical features. Multiple machine learning models were applied, compared, and evaluated to identify the best-performing classifier for spinal condition prediction.

This study consists of a comparative analysis of various machine learning models on vertebral column dataset.

Exploring and implementing the KNN algorithm to predict orthopedic patients with spinal pathology disease using the Vertebral Dataset.

Comparison of scratch-implemented and sklearn nearest neighbors methods for classification of vertebral patient data. Using multiple distance metrics, models were k-value optimized to reduce error rates.