Scaling logistic reg.py

# Scaling the data
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

po = [('scaler',StandardScaler()),('logistic_reg',LogisticRegression(C=10))]
p = Pipeline(po)
lr4 = p.fit(X_train, Y_train)
print("Scaled Accuracy score : ",100*lr4.score(X_test,Y_test),"% \n")

# Logistic Regression coefficients
print("Logistic Regression coefficients",lr3.coef_,"\n")

# Intercept of the model
print("Logistic Regression intercept ",lr3.intercept_)