You have an array of bytestrings; dtype is S:

In [338]: arr=np.array((b'first_element', b'element'))
In [339]: arr
Out[339]: 
array([b'first_element', b'element'], 
      dtype='|S13')

astype easily converts them to unicode, the default string type for Py3.

In [340]: arr.astype('U13')
Out[340]: 
array(['first_element', 'element'], 
      dtype='<U13')

There is also a library of string functions - applying the corresponding str method to the elements of a string array

In [341]: np.char.decode(arr)
Out[341]: 
array(['first_element', 'element'], 
      dtype='<U13')

The astype is faster, but the decode lets you specify an encoding.

See also How to decode a numpy array of dtype=numpy.string_?

This is more of a programming question than a data science question and is therefore better suited for stackoverflow. The vectorizer is trying to convert the string values to lowercase using the .lower() method. However, you are passing in a numpy array, which does not have this method. Instead of passing in a numpy array, you can simply use a pandas dataframe (which does have the .lower() method, see the documentation) by removing the .values attribute in dataset.iloc[:,:-1].values.

The problem is that train_test_split(X, y, ...) returns numpy arrays and not pandas dataframes. Numpy arrays have no attribute named columns

If you want to see what features SelectFromModel kept, you need to substitute X_train (which is a numpy.array) with X which is a pandas.DataFrame.

selected_feat= X.columns[(sel.get_support())]

This will return a list of the columns kept by the feature selector.

If you wanted to see how many features were kept you can just run this:

sel.get_support().sum()  # by default this will count 'True' as 1 and 'False' as 0

Your question leaves a lot unexplained, but the error you're receiving probably came from the last line vectorized_text = features.transform("Heathcare is good").toarray().
The variable features is itself an array (it came from features = ... .toarray()).
I'm assuming you meant to replace it with
vectorized_text = tfidf.transform("Heathcare is good").toarray()?