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.

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

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

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