KNN-Analysis-on-Vertebral-Column-Data-Set

This repository contains a comprehensive Jupyter Notebook that implements a K-Nearest Neighbors (KNN) classification analysis on the Vertebral Column Data Set. The project was developed as part of the Homework 1 assignment for DSCI 552 under the instruction of Mohammad Reza Rajati.

Project Overview

The main objectives of this project are to:

• Perform exploratory data analysis (EDA) on the Vertebral Column Data Set
• Implement KNN classification using various distance metrics:
  • Euclidean
  • Manhattan (via Minkowski with p=1)
  • Minkowski (with varying p values)
  • Chebyshev
  • Mahalanobis
• Evaluate model performance using:
  • Confusion Matrix
  • True Positive Rate (Sensitivity)
  • True Negative Rate (Specificity)
  • Precision
  • F1-score
• Generate learning curves and analyze weighted voting strategies

Dataset

The Vertebral Column Data Set, originally compiled by Dr. Henrique da Mota during a medical residency in Lyon, France, contains six biomechanical attributes:

• pelvic incidence
• pelvic tilt
• lumbar lordosis angle
• sacral slope
• pelvic radius
• grade of spondylolisthesis

For this project, a binary classification task is performed where:

• Normal (NO) is mapped to 0
• Abnormal (AB) is mapped to 1

The dataset is available at the UCI Machine Learning Repository.

Project Structure

• notebook.ipynb
  The main Jupyter Notebook containing the full analysis: data preprocessing, visualization, KNN implementation with different metrics, and evaluation.

• data/
  A folder to store the dataset file(s). Make sure the dataset is placed here if not reading directly from a URL.

• README.md
  This file, which provides an overview of the project and instructions.

Requirements

To run the notebook, you will need:

• Python 3.12.8
• Jupyter Notebook
• The following Python libraries:
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scikit-learn
  • scipy