How do I solve the value error in Python while doing logistic regression?

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Python3.6

<pre>Input:
X_train=train_df.drop("Survived",axis=1)
Y_train=train_df["Survived"]
X_test=test_df.drop("PassengerId",axis=1).copy()

X_train.head()
Y_train.head()
X_test.head()

Output:
Pclass----Sex-----Age-------Parch-----Fare-------EMbarked
3--------- 0 -----34.5------0---------7.82-------2
3--------- 1 -----47 ------0---------7----------0
2--------- 0 -----62 ------0---------9.68-------2
3--------- 0 -----27 ------0---------8.66-------0
3--------- 1 -----22 ------1---------12.2-------0




Input:
X_train.shape,Y_train.shape,X_test.shape

Output:((891, 7), (891,), (418, 6))

input:X_train.head()
output:

Survived---Pclass----Sex----Age-----Parch----Fare----Embarked
0----------3---------0------22-------0--------7.25------0

1----------1---------1------38-------0--------71.2833----1

1----------3---------1-------26------0--------7.925------0

1----------1---------1-------35------0---------53.1------0

0----------3---------0-------35------0---------8.05------0

# Logistic Regression

logreg = LogisticRegression()
logreg.fit(X_train, Y_train)
Y_pred = logreg.predict(X_test)
acc_log = round(logreg.score(X_train, Y_train) * 100, 2)
acc_log

c:\users\user\appdata\local\programs\python\python37\lib\site-packages\sklearn\linear_model\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
  FutureWarning)

ValueError                                Traceback (most recent call last)
<ipython-input-64-5854ca91fc64> in <module>
      3 logreg = LogisticRegression()
      4 logreg.fit(X_train, Y_train)
----> 5 Y_pred = logreg.predict(X_test)
      6 acc_log = round(logreg.score(X_train, Y_train) * 100, 2)
      7 acc_log

c:\users\user\appdata\local\programs\python\python37\lib\site-packages\sklearn\linear_model\base.py in predict(self, X)
    287             Predicted class label per sample.
    288         """
--> 289         scores = self.decision_function(X)
    290         if len(scores.shape) == 1:
    291             indices = (scores > 0).astype(np.int)

c:\users\user\appdata\local\programs\python\python37\lib\site-packages\sklearn\linear_model\base.py in decision_function(self, X)
    268         if X.shape[1] != n_features:
    269             raise ValueError("X has %d features per sample; expecting %d"
--> 270                              % (X.shape[1], n_features))
    271 
    272         scores = safe_sparse_dot(X, self.coef_.T,

ValueError: X has 6 features per sample; expecting 7

What I have tried:

I tried deleting the Survived column from the train data frame but still it is of no use.
Moreover dropping the Passengerid column from the test data frame removes it from the data frame but dropping the survived column from the train data frame doesn't remove it.