<prompt> Etsa sehlopha ho fumana hore na lengolo-tsoibila le fanoeng ke spam kapa eseng spam.nan  <response>: Instruction in English: Create a classifier to determine if a given email is spam or not spam. . Response in English: Sure, to accomplish this task, we would first need to gather and preprocess a labeled dataset of emails, where each email is tagged as either spam or not spam. The next step would be to extract the relevant features from these emails and use them as inputs for our classifier.Heres an outline of the general steps we would take to create a spam email classifier: Data Collection and PreprocessingFirst, we need a dataset of labeled emails to train our classifier. We can use pre-existing datasets such as the SpamAssassin corpus or the Enron-Spam dataset. Once we have gathered the data, we need to preprocess it. Here the steps generally included:- Cleaning the raw data: removing any irrelevant information, formatting the text uniformly.- Tokenization: breaking the email text into individual words or tokens.- Stop words removal: removing common words such as and, the, is, etc., which dont provide much value in classification.- StemmingLemmatization: reducing words to their base or root form. Feature ExtractionAfter preprocessing the data, we need to extract relevant features from the email text, which we will use to train our classifier. Some of the features we can use are:- Word frequency: the number of times specific words appear in the email.- Special characters: the number of special characters in the email, such as , !, etc.- Email length: length of the email.- Capitalization: the number of capitalized words in the email.- Hyperlinks: the number of hyperlinks in the email. Model Selection and TrainingNow that we have extracted the features from our data, we need to select a classifier model to use. Some popular options for this task include Naive Bayes, Decision Trees, and Support Vector Machines SVM. We need to train our classifier model on our preprocessed dataset, using the features extracted above as input. Model EvaluationOnce our model is trained, we need to evaluate its performance. We can do this by dividing our dataset into a training and a testing set. We train our classifier on the training set and test it on the testing set. We can then compute classification metrics such as accuracy, precision, recall, and F1-score to evaluate our models performance.By following these steps, we can create a classifier that can determine if a given email is spam or not spam. . Response in Sesotho: Ehlile, ho phetha mosebetsi ona, re tla tlameha ho qala ka ho bokella le ho lokisa esale pele lethathamo la li-imeile tse ngolisitsoeng, moo lengolo-tsoibila le tšoailoeng e le spam kapa eseng spam. Mohato o latelang e tla ba ho ntša likarolo tse amehang ho tsoa ho li-imeile tsena le ho li sebelisa e le lintlha tsa mohlophisi oa rona. Mona ke lethathamo la mehato e akaretsang eo re ka e nkang ho theha sehlopha sa lengolo-tsoibila la spam: Pokello ea Lintlha le ho Hlophisa Esale Pele, re hloka pokello ea lintlha tsa mangolo-tsoibila a ngotsoeng ho koetlisa mohlophisi oa rona. Re ka sebelisa li-database tse seng li le teng joalo ka SpamAssassin corpus kapa dataset ea Enron-Spam. Ha re se re bokelletse data, re hloka ho e lokisa esale pele. Mona mehato ka kakaretso e ne e kenyelletsa: - Ho hloekisa data e tala: ho tlosa tlhahisoleseling efe kapa efe e sa lokelang, ho fometa mongolo ka mokhoa o tsoanang. - Tokenization: ho arola mongolo oa lengolo-tsoibila hore e be mantsoe kapa li-tokens. - Emisa ho tlosa mantsoe: ho tlosa mantsoe a tloaelehileng a kang le, the, ke, joalo-joalo, a sa faneng ka boleng bo bongata ka lihlopha. - Stemming Lemmatization: ho fokotsa mantsoe ho ea motheong oa ona kapa sebopeho sa motso. Feature Extraction Ka mora ho lokisa lintlha esale pele, re hloka ho ntša lintlha tse amehang ho tsoa mongolong oa lengolo-tsoibila, tseo re tla li sebelisa ho koetlisa mohlophisi oa rona. Tse ling tsa likarolo tseo re ka li sebelisang ke: - Khafetsa ea mantsoe: palo ea makhetlo ao mantsoe a ikhethileng a hlahang ho lengolo-tsoibila. - Litlhaku tse khethehileng: palo ea litlhaku tse khethehileng ho lengolo-tsoibila, joalo ka , !, joalo-joalo - Bolelele ba lengolo-tsoibila: bolelele ba lengolo-tsoibila. - Capitalization: palo ea mantsoe a litlhaku tse kholo ho lengolo-tsoibila. - Li-hyperlink: palo ea li-hyperlink ho lengolo-tsoibila. Khetho ea Mohlala le Koetliso Kaha joale re ntšitse likarolo ho data ea rona, re hloka ho khetha mofuta oa lihlopha oo re tla o sebelisa. Likhetho tse ling tse tsebahalang bakeng sa mosebetsi ona li kenyelletsa Naive Bayes, Lifate tsa Liqeto, le Mechini ea Tsehetso ea Vector SVM. Re hloka ho koetlisa mofuta oa rona oa sehlopha ho datha tsa rona tse seng li hlophisitsoe, re sebelisa likarolo tse ntšitsoeng kaholimo joalo ka tlhahiso. Tekolo ea Mohlala Hang ha mohlala oa rona o koetlisoa, re hloka ho lekola tšebetso ea ona. Re ka etsa sena ka ho arola dataset ea rona ka koetliso le sete ea liteko. Re koetlisetsa sehlopha sa rona ka sete ea koetliso le ho e leka ka sete ea liteko. Joale re ka kopanya metrics ea likarolo tse kang ho nepahala, ho nepahala, ho hopola, le lintlha tsa F1 ho lekola tšebetso ea mohlala oa rona. Ka ho latela mehato ena, re ka theha sehlopha se ka tsebang hore na lengolo-tsoibila le fanoeng ke spam kapa ha se spam. 