from sklearn.linear_model import LogisticRegression
import numpy as np
x1 = np.random.randn(100)
x2 = x1 + 0.00001*np.random.randn(100)
x3 = np.random.randn(100)
y = ((100*x1 - 100*x2 + x3) > 0).astype(int)
X = np.vstack([x1, x2, x3]).T

m = LogisticRegression().fit(X, y)
print(np.std(X, 0)*m.coef_)

>> [[0.02823824 0.02824964 3.70616255]]