Today, in the USA, about one in eight women has a risk of suffering from breast cancer over their life time. Hence we need to dig out the relevant variables that can predict malignant tumor.
This data can be found on UC Irvine Machine Learning Repository.
Online notebook
This dataset contains:
Attribute Information:
- ID number
- Diagnosis (M = malignant, B = benign)
Ten real-valued features are computed for each cell nucleus:
- radius (mean of distances from center to points on the perimeter)
- texture (standard deviation of gray-scale values)
- perimeter
- area
- smoothness (local variation in radius lengths)
- compactness (perimeter^2 / area - 1.0)
- concavity (severity of concave portions of the contour)
- concave points (number of concave portions of the contour)
- symmetry
- fractal dimension (“coastline approximation” - 1)