This project was completed as part of a Machine Learning assignment during my bachelor's degree, implemented using Python in Jupyter Notebook. The goal was to build a K-Nearest Neighbour (KNN) classifier to predict whether a cancer case is malignant or benign using a provided dataset. The dataset contains 32 features extracted from digitized images of fine needle aspirates (FNA) of breast masses, representing various characteristics of the cell nuclei.

The workflow involved loading the dataset, splitting it into 70% training and 30% testing sets, applying feature scaling, training the KNN model, generating predictions, and evaluating the model’s overall performance.