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SMachine Learning Nummpy Array Sample

Question

Dataset

The records are stored in a text file named “medical_records.data”. Each row corresponds to a patient record. The diagnosis is the attribute predicted. In this dataset, the diagnosis is the second field and is either B (benign) or M (malignant). There are 32 attributes in total (ID, diagnosis, and 30 real-valued input features

Attribute Information
1. ID number
2. Diagnosis (M = malignant, B = benign) 3-32
Ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) parameter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry j) 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. All feature values are recorded with four significant digits.

Your Tasks
Preprocessing Complete the function prepare_dataset that loads the data records from the text file, and converts the information to numpy arrays.
Build classifiers

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