<prompt> Theha lenaneo ho bolela esale pele hore na lengolo-tsoibila ke spam ka lebaka la mongolo oa lona.nan  <response>: Instruction in English: Create a program to predict whether an email is spam given its text. . Response in English: 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. . Response in Sesotho: Mohlala ke ona o bonolo oa lenaneo la ho bolela esale pele ka lengolo-tsoibila la spam le sebelisang Python: python import re from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB Mosebetsi oa ho sebetsana le mongolo oa lengolo-tsoibila def process_texttext: Tlosa e khethehileng litlhaku text re.suba-zA-Z, , text kgutlisa mongolo Koetlisa mohlala ka li-imeile tsa mohlala Hey, pizza ea mahala bosiung bona!, Wife 1000 ka ho tobetsa sehokelo sena, Lumela, u phela joang?, Fumana ka potlako ka ho tsetela lenaneong la rona, Seboka se reriloe bocha bakeng sa hosane labels spam, spam, not spam, spam , eseng spam vectorizer CountVectorizerstop_wordsenglish vectorizer.fitli-imeile X vectorizer.transformli-imeile classifier MultinomialNB classifier.fitX, labels Mosebetsi oa ho bolela esale pele haeba imeile ke spam kapa che def is_spamemail_text: email_text process_textemail_text x_test vectorizer.transformemail_text prediction classifier.predictx_test ponelopele ea ho khutla0 email_text U hapile leeto la mahala ho ea Hawaii result is_spamemail_text printsephetho Lenaneo lena le sebelisa mekhoa ea Natural Language Processing ho koetlisa sehlopha sa Naive Bayes ka mohlala o monyane oa li-imeile tse nang le lileibole tse tsamaellanang le tsona spam kapa e seng spam. Ebe e sebelisa sehlopha sena ho bolela esale pele hore na lengolo-tsoibila le lecha ke spam kapa che. Ha e le hantle, ena ke mohlala oa motheo feela, me ka tsebetso, mohlala o lokela ho koetlisoa ho dataset e kholoanyane le e emelang ho ntlafatsa ho nepahala ha eona. Hape, ho ka sebelisoa li-algorithms le mekhoa e fapaneng ho ntlafatsa sephetho se boletsoeng esale pele. 