This repository contains a Python script demonstrating how to build a machine learning pipeline for predicting breast cancer using the Breast Cancer Wisconsin dataset. The goal is to classify tumors as either benign or malignant, with a focus on minimizing false negatives and false positives. We compare the performance of an XGBoost model with other popular models, such as logistic regression, random forest, and support vector machines (SVMs), using common evaluation metrics like accuracy, precision, recall, and F1-score.
The code for this project can be found in the breastcancer_ml.ipynb file in this repository.