Scikit-learn: ValueError: X has 1007 features, but StandardScaler is expecting 1016 features as input

Question

When trying to predict the outcome for a test dataset utilizing the pipeline fitted on a training dataset, I am receiving an error message saying:

Traceback (most recent call last):
  File "c:\Users\username\Desktop\Project\PythonTool\calculator\database-analyzer\database_analyzer.py", line 380, in <module>
    main()
  File "c:\Users\username\Desktop\Project\PythonTool\calculator\database-analyzer\database_analyzer.py", line 349, in main
    bestmodel_predictor(train_original, train_data, test_original)
  File "c:\Users\username\Desktop\Project\PythonTool\calculator\database-analyzer\utilities_module.py", line 1768, in bestmodel_predictor
    test_original[TARGET_VARIABLE] = predictor_model(train_original,
  File "c:\Users\username\Desktop\Project\PythonTool\calculator\database-analyzer\utilities_module.py", line 1734, in predictor_model
    predictions = np.rint(pipe_abc.predict(test_dataset))
  File "C:\ProgramData\Anaconda3\envs\tf\lib\site-packages\sklearn\utils\metaestimators.py", line 113, in <lambda>
    out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)  # noqa
  File "C:\ProgramData\Anaconda3\envs\tf\lib\site-packages\sklearn\pipeline.py", line 469, in predict
    Xt = transform.transform(Xt)
  File "C:\ProgramData\Anaconda3\envs\tf\lib\site-packages\sklearn\preprocessing\_data.py", line 973, in transform
    X = self._validate_data(
  File "C:\ProgramData\Anaconda3\envs\tf\lib\site-packages\sklearn\base.py", line 585, in _validate_data
    self._check_n_features(X, reset=reset)
  File "C:\ProgramData\Anaconda3\envs\tf\lib\site-packages\sklearn\base.py", line 400, in _check_n_features
    raise ValueError(
ValueError: X has 1007 features, but StandardScaler is expecting 1016 features as input.

Here is a portion of my code including the problematic lines which I am not sure how to fix in order to overcome the mismatch between what StandardScaler takes with the available features of X:

def predictor_model(some_df: pd.DataFrame, test_dataset: list) -> list:
    """This function applied prediction algorithm on test dataset based on learned train dataset

    Args:
        some_df (pd.DataFrame): _description_
        test_dataset (list): _description_

    Returns:
        list: _description_
    """
    pipe_abc = pipeline_maker()
    features_final, target_final = featuretarget_separator(some_df)
    pipe_abc.fit(features_final, target_final)
    predictions = np.rint(pipe_abc.predict(test_dataset))
    return predictions


def bestmodel_predictor(train_original: pd.DataFrame, train_data: pd.DataFrame,
                        test_original: pd.DataFrame) -> pd.DataFrame:
    """This function predics the class of a test dataset with
       missing target

    Args:
        some_df (pd.DataFrame): _description_
        train_data (pd.DataFrame): _description_
        test_original (pd.DataFrame): _description_

    Returns:
        pd.DataFrame: _description_
    """
    # Align "train" and "test" datasets
    test_original[TARGET_VARIABLE] = 0
    test_original = test_original[train_original.columns]

    # making predictions
    test_original = test_original.iloc[:, :-1]
    test_original[TARGET_VARIABLE] = predictor_model(train_original,
                                                     test_dataset)
    test_prediction = test_original[test_original.columns.intersection(
        train_data.columns)]
    #save your predictions to a csv file
    df2csv(test_prediction, WORKING_DIR / Path(TEST_PREDICTION), True, False)


def featuretarget_separator(some_df: pd.DataFrame) -> tuple:
    """This function separates a dataframe into its features and target for later ML modelling

    Args:
        train_original (pd.DataFrame): _description_

    Returns:
        tuple: _description_
    """
    return (some_df.values[:, :len(some_df.columns) - 1],
            some_df.values[:, len(some_df.columns) - 1])


def pipeline_maker() -> Pipeline:
    """This function returns a pipeline for the best-fit model

    Returns:
        object: _description_
    """
    steps = [('scaler', StandardScaler(with_mean=False)),
             ('rfc',
              RandomForestClassifier(random_state=SEED,
                                     n_estimators=105,
                                     max_features=19,
                                     max_depth=2))]
    return Pipeline(steps)