<prompt> Create a program to predict whether an email is spam given its text.  <response>: Here is a simple example of a spam email prediction program using Python:pythonimport refrom sklearn.feature_extraction.text import CountVectorizerfrom sklearn.naive_bayes import MultinomialNB Function to process the email textdef process_texttext: Remove special characters text re.suba-zA-Z, , text return text Train the model with example emailsemails Hey, free pizza tonight!, Win 1000 by clicking this link, Hello, how are you?, Get rich quick by investing in our program, Meeting rescheduled for tomorrowlabels spam, spam, not spam, spam, not spamvectorizer CountVectorizerstop_wordsenglishvectorizer.fitemailsX vectorizer.transformemailsclassifier MultinomialNBclassifier.fitX, labels Function to predict if an email is spam or notdef is_spamemail_text: email_text process_textemail_text x_test vectorizer.transformemail_text prediction classifier.predictx_test return prediction0email_text You have won a free trip to Hawaiiresult is_spamemail_textprintresultThis program uses Natural Language Processing techniques to train a Naive Bayes classifier on a small set of example emails with their corresponding labels spam or not spam. It then applies this classifier to predict if a new email is spam or not.Of course, this is just a basic example, and in practice, the model should be trained on a much larger and more representative dataset to improve its accuracy. Also, different algorithms and techniques can be applied to improve the prediction results. 