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- # Select the columns to use for prediction in the neural network
- prediction_var = ['radius_mean', 'texture_mean', 'perimeter_mean',
- 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',
- 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',
- 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',
- 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
- 'fractal_dimension_se', 'radius_worst', 'texture_worst',
- 'perimeter_worst', 'area_worst', 'smoothness_worst',
- 'compactness_worst', 'concavity_worst', 'concave points_worst',
- 'symmetry_worst', 'fractal_dimension_worst']
- X = data[prediction_var].values
- Y = data.diagnosis.values
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