Hands-On KNN: A Step-by-Step Guide to Nearest Neighbor Classification

Join us for an engaging, hands-on workshop where we’ll explore the powerful K-Nearest Neighbors (KNN) algorithm for classification, one of the most intuitive and effective techniques in machine learning. This session is designed for beginners—no prior programming experience or knowledge of statistics or machine learning is required. We’ll walk you through each step of the KNN process, from understanding the core concepts to applying them to real-world data using R and RStudio.

In this workshop, you'll learn how KNN works by classifying data points based on the closest neighbors and how to select the optimal number of neighbors for your dataset. Through interactive exercises and guided examples, we’ll cover key concepts like distance metrics, feature scaling, model evaluation, and more. You’ll gain the confidence to implement KNN for basic classification tasks in R.

Participants should have R and RStudio installed on their computers prior to the session. No advanced coding or statistical knowledge is necessary, but we recommend having a curiosity for learning about data analysis and machine learning in a practical, hands-on environment. By the end of this workshop, you'll be equipped to apply KNN classification techniques in your own projects with ease.

Speaker Biography:

I am Kristopher Wilson, a first-year Ph.D. student in Statistics here at NC State University. A native of Atlanta, Georgia, I earned my B.S. in Statistics with a minor in Industrial Engineering in 2024. During my undergraduate years at NC State, I gained valuable experience working with the Academic Support Program for Student Athletes (ASPSA), TRIO Collegiate Programs, and University Housing. Beyond my academic pursuits, I am an avid sports fan and enjoy immersing myself in books and music, particularly playing the violin. I look forward to contributing to the field of statistics and continuing my personal and professional growth at NC State.