Breast Cancer Prediction using KNN Classifier Algorithm.
The objective of these predictions is to assign patients to either a benign group that is noncancerous or a malignant group that is cancerous.
The experimental study is based on the Wisconsin Breast Cancer database from the UC Irvine Machine Learning Repository.
The Breast Cancer database was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. It contains 699 instances, 458 (65.5%) benign and 241 (34.5%) malignant cases. Each case is characterized by 9 attributes as described by Table I and two classes (benign and malignant).
Attributes and domains are as follows:
Clump Thickness: 1 – 10 Uniformity of Cell Size: 1 – 10 Uniformity of Cell shape: 1 – 10 Marginal Adhesion: 1 – 10 Single Epithelial Cell Size: 1 – 10 Bare Nuclei: 1 – 10 Bland Chromatin: 1 – 10 Normal Nucleoli: 1 – 10 Mitoses: 1 – 10
- Python version used in this project: 3.5+
To run this project you will require Python, Scikit-Learn and Matplotlib installed in your system. Since the installation of Scikit-Learn is a little trivial it is better to install Anaconda instead since it comes with scikit-learn, numpy and scipy preinstalled with it.
PyCharm but any other editor can also be used if you like for instance Sublime Text.
MIT License
Copyright (c) 2017 Luka Anicin
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.