Logistic Regression Model has been used to predict the chances of Breast Cancer.
About Dataset
Attribute Information:-
- id
- diagnosis: M = malignant, B = benign
Columns 3 to 32 Ten real-valued features are computed for each cell nucleus:
- radius: 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 The mean, standard error, and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius.