<jupyter_start><jupyter_text><jupyter_code>import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC #Support Vector Classifier
iris = datasets.load_iris()
X = iris["data"][:, (2, 3)] # this is the petal length and width
y = (iris["target"] == 2).astype(np.float64) #boolean of whether the flower is an iris virginica or not as a float 64
iris
plt.scatter(iris["data"][:,2], iris["data"][:,3])
plt.show()
y
model = Pipeline([
    ("scaler", StandardScaler()),
    ("linear_svc", LinearSVC(C=1, loss="hinge"))
])

model.fit(X, y)
model.predict([[5.5, 1.7]])<jupyter_output><empty_output>
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