Exploring the Wisconsin Breast Cancer data set (which was never actually intended for machine learning) and optimizing Support Vector Machine models to classify benign and malignant tumors.
In this project we will be using SVMs on the Wisconsin Breast Cancer dataset which can be found at the following URL:
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29
This URL contains all relevant information on how the data is formatted. The data was collected by Dr. William H. Wolberg from the University of Wisconsin Hospital between 1989 and 1991. The dataset was never actually intended for any machine learning to be done on it, but you will be seeing how well you can get an SVM to classify the data.
Tools used for analysis and classifaction are: Python programming language, jupyter notebook, numpy, seaborn, sklearn, and matplotlib.