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.

What is the primary purpose of using Google Collab in this tutorial?

• To store large datasets

• To create visualizations

• To edit video tutorials

• To run, write, and share code

Why is it important to drop the ID column during data preprocessing?

• It is a categorical feature

• It is not needed for analysis or modeling

• It is already normalized

• It contains sensitive information

What is the purpose of normalizing numerical features in the dataset?

• To improve data visualization

• To convert categorical data to numerical

• To ensure no feature outweighs another

• To reduce the size of the dataset

What is the significance of encoding the diagnosis feature?

• To make it compatible with numerical operations

• To improve visualization

• To reduce the number of features

• To increase the dataset size

What percentage of the dataset is used for testing in this tutorial?

• 40%

• 30%

• 10%

• 20%

What does the PCA graph illustrate in the context of the KNN model?

• The accuracy of the model

• The decision boundary between classes

• The preprocessing steps

• The training dataset size

Why might some values of K be more effective than others in KNN?

• They increase the number of features

• They reduce the dataset size

• They improve the model's accuracy, precision, and recall

• They simplify the preprocessing steps