MaksimEkin / Breast-Cancer-Prediction-SVM

Support Vector Machines on the "Breast Cancer Wisconsin (Diagnostic) Data Set" for breast cancer prediction.

Support Vector Machines on the Breast Cancer Wisconsin (Diagnostic) Data Set

Attribution: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, by Aurelien Geron (O'Reilly). Copyright 2019 Kiwisoft S.A.S, 978-1-492-03264-9

Machine Learning Practice. Implimenting the project following the Chapter-5 on O'REILLY's Hands-On Machine Learning.

Goal: Predict breast cancer given all other values with the use of SVMs.
Approach:

1. Supervised Learning task, because given labeled traning examples.
2. Classification task.
3. There is no continuous flow of data, no need to adjust to changing data, and the data is small enough to fit in memmory: Batch Learning

Data: Breast Cancer Wisconsin (Diagnostic) Data Set | Kaggle
Project Author: Maksim Ekin Eren

SVM's goal is to have largest possible margin between decision boundary that separetes the classes and the training instances.