Breast Cancer Wisconsin (Diagnostic) Data Set
This is a popular dataset that contains columns that might be useful to determine if a tumor is breast cancer or not. There are a total of 32 columns and 569 rows. This dataset is used in class to introduce binary (two class) classification. The following fields are present:
- id - Identity column, not really useful to a neural network.
- diagnosis - Diagnosis, B=Benign, M=Malignant.
- mean_radius - Potentially predictive field.
- mean_texture - Potentially predictive field.
- mean_perimeter - Potentially predictive field.
- mean_area - Potentially predictive field.
- mean_smoothness - Potentially predictive field.
- mean_compactness - Potentially predictive field.
- mean_concavity - Potentially predictive field.
- mean_concave_points - Potentially predictive field.
- mean_symmetry - Potentially predictive field.
- mean_fractal_dimension - Potentially predictive field.
- se_radius - Potentially predictive field.
- se_texture - Potentially predictive field.
- se_perimeter - Potentially predictive field.
- se_area - Potentially predictive field.
- se_smoothness - Potentially predictive field.
- se_compactness - Potentially predictive field.
- se_concavity - Potentially predictive field.
- se_concave_points - Potentially predictive field.
- se_symmetry - Potentially predictive field.
- se_fractal_dimension - Potentially predictive field.
- worst_radius - Potentially predictive field.
- worst_texture - Potentially predictive field.
- worst_perimeter - Potentially predictive field.
- worst_area - Potentially predictive field.
- worst_smoothness - Potentially predictive field.
- worst_compactness - Potentially predictive field.
- worst_concavity - Potentially predictive field.
- worst_concave_points - Potentially predictive field.
- worst_symmetry - Potentially predictive field.
- worst_fractal_dimension - Potentially predictive field.
The following code shows 10 sample rows.