This Machine Learning model was build with the objective to better predict if a Brest Cancer is either Benign or Malignant, based on many features. To train and test the model, it was used the 'Breast Cancer Wisconsin (Diagnostic) Database', as a source, containing information for every register of:
- 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)

To build the model, it was performed the PCA - to reduce the model into 2 componets (so we could see the labels in a 2D scatterplot) - and the SVM classifier - to predict the Cancer class (1: Malignant | 0: Benign)