breast-cancer-classification

Predict if a cancer diagonsis is malignant or benign by classifying several features and observation.

Data Set Characteristics:

Number of Instances: 569

Number of Attributes: 30 numeric, predictive attributes and the class

Attribute Information:

• 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)

  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.

  • class:
    • WDBC-Malignant
    • WDBC-Benign

Based on the tutorial by Dr. Ryan Ahmed