Breast Cancer Classification Using KNN & SVM

Author: @pathananas2007
Dataset: Built-in scikit-learn Breast Cancer dataset
Course: IBM Machine Learning with Python (Coursera)

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Project Overview

This project applies classification models to predict whether breast tumors are benign or malignant. The notebook implements:

• Data loading and exploration
• Feature scaling and preprocessing
• Gaussian noise injection to test model robustness
• Training and evaluation using:
  • K-Nearest Neighbors (KNN)
  • Support Vector Machine (SVM)
• Performance evaluation with:
  • Accuracy scores
  • Classification reports
  • Confusion matrices
• Visualizations of feature comparisons (original vs noisy) and confusion matrices
• Comparison of training vs testing performance

This project was completed as part of the IBM Machine Learning with Python course on Coursera. It demonstrates hands-on application of classification models for predicting breast cancer.

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Dataset

The dataset is included in scikit-learn. It contains:

• 30 numeric features about tumor characteristics
• Labels: 0 = benign, 1 = malignant
• Target names: ['malignant', 'benign']
• No external data download is required

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Project Visualizations

Feature Noise Comparison

Confusion Matrices (Testing Data)

Overall Performance Comparison

Confusion Matrices (Training Data)

Training vs Testing Accuracies

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Installation

Clone this repository:

git clone https://github.com/pathananas2007/Breast-Cancer-Classification.git
cd Breast-Cancer-Classification