機械学習のエラーについて

前提・実現したいこと

最後予測するところでエラーが出ます

発生している問題・エラーメッセージ

ValueError                                Traceback (most recent call last)
<ipython-input-106-badfaf7f9db2> in <module>
      2 lr = LogisticRegression()
      3 lr.fit(x_train, y_train)
----> 4 lr.predict(x_test)

/opt/conda/lib/python3.6/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)

/opt/conda/lib/python3.6/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 4 features per sample; expecting 5

該当のソースコード

# 欠損値の補完
train_age_mean = train['Age'].mean()
train.fillna(value={'Age':train_age_mean}, inplace=True)
train['Age'] = train['Age'].astype(int)
# 特徴量の削除
train.drop('PassengerId', axis=1, inplace=True)
train.drop('Name', axis=1, inplace=True)
train.drop('Ticket', axis=1, inplace=True)
train.drop('Cabin', axis=1, inplace=True)
train.drop('Embarked', axis=1, inplace=True)
# 特徴量の値の変化
train.replace({'male':0, 'female':0}, inplace=True)
# 特徴量エンジニアリング
train['familysize'] = train['SibSp'] + train['Parch'] + 1
train.drop('SibSp', axis=1, inplace=True)
train.drop('Parch', axis=1, inplace=True)
#train['Fare'] = train['Fare'].astype(int)
train.drop(train.columns[np.isnan(train).any()], axis=1, inplace=True)

# 欠損値の補完
test_age_mean = test['Age'].mean()
test.fillna(value={'Age':test_age_mean}, inplace=True)
test['Age'] = test['Age'].astype(int)
# 特徴量の削除
test.drop('PassengerId', axis=1, inplace=True)
test.drop('Name', axis=1, inplace=True)
test.drop('Ticket', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
test.drop('Embarked', axis=1, inplace=True)
# 特徴量の値の変化
test.replace({'male':0, 'female':0}, inplace=True)
# 特徴量エンジニアリング
test['familysize'] = test['SibSp'] + test['Parch'] + 1
test.drop('SibSp', axis=1, inplace=True)
test.drop('Parch', axis=1, inplace=True)
#train['Fare'] = train['Fare'].astype(int)
test.drop(test.columns[np.isnan(test).any()], axis=1, inplace=True)

train

# 説明変数と目的変数の定義
train = train[train.columns[::-1]]
x_train = train.loc[:, :'Pclass']
y_train = train.loc[:, 'Survived']
x_test = test
x_test

# モデルの作成
lr = LogisticRegression()
lr.fit(x_train, y_train)
lr.predict(x_test)