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import numpy
X = numpy.array([3.78, 2.44, 2.09, 0.14, 1.72, 1.65, 4.92, 4.37, 4.96, 4.52, 3.69,
5.88]).reshape(-1,1)
print(X)
y = numpy.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
from sklearn import linear_model
logr = linear_model.LogisticRegression()
logr.fit(X,y)
predicted = logr.predict(numpy.array([3.46]).reshape(-1,1))
print(predicted)
logr = linear_model.LogisticRegression()
logr.fit(X,y)
log_odds = logr.coef_
odds = numpy.exp(log_odds)
print(odds)
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