Apply Machine Learning on a Cancer Dataset

Apply Machine Learning on a Cancer Dataset

Apply Machine Learning on a Cancer Dataset. In this article, take a look at how to apply machine learning on a cancer dataset.


Support Vector Machines (SVM) are one of the most popular supervised learning methods in Machine Learning(ML). Many researchers have reported superior results compared with older ML techniques.

SVM can be applied on regression problems as well as classification problems, however, here I describe a classification application on a cancer dataset.


SVM has been widely used throughout ML, including medical research, face recognition, spam email, document classification, handwriting recognition. In the medical field, SVM has been applied by practitioners in:

  • White blood cells classification
  • Cancer prediction
  • Identifying gene classification 

Researchers have claimed better results than logistic regression and decision trees and also Neural Networks.

SVM Linear Applications

Overview of method

A popular classifier for linear applications because SVM’s have yielded excellent generalization performance on many statistical problems with minimal prior knowledge and also when the dimension of the input space(features) is very high.

python machine learning ai artificial intelligence sklearn svm

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