<jupyter_start><jupyter_text>Load dataset from datasets<jupyter_code>iris = datasets.load_iris()
data = iris.data
targets = iris.target
# Here train and test data are not splitted and whole dataset is transformed/normalized
preprocessor = preprocessing.StandardScaler()
data = preprocessor.fit_transform(data)
np.unique(targets)
# https://matplotlib.org/stable/api/markers_api.html
markers = ['x', 'o', '^']

for target in np.unique(targets):
    x_data = data[targets == target]
    plt.scatter(x_data[:,2], x_data[:, 3], marker=markers[target])
plt.xlabel('X2 feature')
plt.ylabel('X3 feature')<jupyter_output><empty_output>
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