We are provided a data set which includes 10 features, one of which is malignancy, called Class. The purpose of this exploration is to identify how we can use the 9 other features to predict cancer malignancy. We will also perform some exploratory analysis on the data set. This study was conducted to learn if a new method called fine needle aspiration (which draws only a small tissue sample) could be effective in determining tumor status and prognosis. We take advantage of this study to explore the power of logistic regression. The features include:
- Class - 0 if malignant, 1 if benign
- Adhesion - marginal adhesion
- BNuclei - bare nuclei
- Chromat - bland chromatin
- Epithel - epithelial cell size
- Mitoses - mitoses
- NNucleo - normal nucleoli
- ClThick - clump thickness
- UShape - cell shape uniformity
- UCSize - cell size uniformity
The code is availble in the repository. The study is available here
