You are using the right method but in a wrong way :)

nan_to_num is a method of numpy module, not numpy.ndarray. So instead of calling nan_to_num on you data, call it on numpy module giving your data as a paramter:

import numpy as np
data = np.array([1,2,3,np.nan,np.nan,5])
data_without_nan = np.nan_to_num(data)

prints:

array([1., 2., 3., 0., 0., 5.])

In your example:

import numpy as np
val = np.nan_to_num(setTo.ravel())

It would be helpful if you could post the full stack trace, so that we can see which line your error occurs at. In general, the more information you can provide in a question, the better.

In this case, it looks like your full_model_pipeline may somehow become a numpy array. Since you have a one-element pipeline, you could try changing

full_model_pipeline = Pipeline(steps =[
                        ('full_pipeline',full_pipeline),
                        ('model',LinearRegression())
                    ])

full_model_pipeline.fit(X_train,y_train)

to

model = LinearRegression()
model.fit(X_train, y_train)

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.

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()?