Breast Cancer Detection

Goal

The goal is to use Machine Learning to classify the Cancer as Benign or Malignant.

Data

I used the data set from the University of California Irvine Machine Learning Repository.

Environment and tools

1. Jupyter Notebook
2. Numpy
3. Pandas
4. Matplotlib
5. Scikit-learn

Features Used in predicting the results

1. Clump thickness
2. Uniform cell size
3. Uniform cell shape
4. Marginal adhesion
5. Signal epithelial size
6. Bare nuclei
7. Bland chromatin
8. Normal nucleoli
9. Mitoses

I chose both Knearest Neighbors as well as Support Vector Machines Model to train the data.

I got a 96.4% accuracy by using the KNearest Neighbors strategy.