Complete Hands-On K-NN Workshop | Train-Test Split, Model Training & Evaluation Explained

Complete Hands-On K-NN Workshop | Train-Test Split, Model Training & Evaluation Explained

In this complete hands-on workshop, you’ll learn the full K-Nearest Neighbors (K-NN) workflow, from preparing data to training and evaluating a machine learning model. This practical session is designed to help beginners and intermediate learners understand how K-NN works step by step using real-world examples.

You’ll walk through:

Understanding the K-NN algorithm and distance metrics

Preparing datasets and performing train-test splits

Training a K-NN model with optimal hyperparameters

Evaluating model performance using accuracy, confusion matrix, and metrics

Understanding overfitting, underfitting, and the impact of K values

This workshop focuses on practical implementation, making it ideal for students, data science beginners, and professionals revisiting core machine learning concepts. By the end of the session, you’ll be confident in applying K-NN to classification problems and interpreting results correctly.

Whether you’re learning machine learning for exams, projects, or real-world applications, this hands-on guide will strengthen your fundamentals and boost your confidence.

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KNN algorithm, K-nearest neighbors, machine learning KNN, KNN workflow, train test split machine learning, KNN model training, KNN evaluation, classification algorithms, data science basics

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