PY
py
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n = int(input())
X = []
for i in range(n):
X.append([float(x) for x in input().split()])
if i == 0 :
p = len(X[0])
y = [int(x) for x in input().split()]
datapoint = [float(x) for x in input().split()]
import numpy as np
from sklearn.linear_model import LogisticRegression
X = np.array(X)
X = X.reshape((n, p))
y = np.array(y)
model = LogisticRegression()
model.fit(X, y)
datapoint = np.array(datapoint)
datapoint = datapoint.reshape((1, p))
z = model.predict(datapoint)
print(z[0])
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OUTPUT
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