Breast Cancer Diagnosis with Python & KNN: A Step-by-Step Coding Tutorial

This tutorial provides a hands-on guide to implementing the K Nearest Neighbours (KNN) algorithm using Google Collab. It covers data preprocessing, feature scaling, and encoding, followed by splitting the data into training and testing sets. The tutorial demonstrates training a KNN model to classify breast tumor cells as benign or malignant, evaluates the model's accuracy, and visualizes the results using PCA. It concludes with optimizing the K value for improved model performance.

Describe the process of encoding categorical features in the data set.

What is the role of PCA in visualizing the model's performance?

What factors should be considered when selecting the value of K in KNN?