<prompt> Etsa algorithm bakeng sa ho arola mangolo-tsoibila ho ba spam eseng spam.nan  <response>: Instruction in English: Design an algorithm for classifying emails into spam and not-spam. . Response in English: Possible algorithm for classifying emails into spam and not-spam could include the following steps:1. Extract relevant features: Extract the relevant features from the email that can be helpful in classification such as the senders address, subject line, email body, links, and attachments.2. Preprocess the data: Perform text preprocessing techniques like tokenization, stop-word removal, and stemming to the email body and subject line to obtain clean data.3. Create a training dataset: Create a labeled training dataset with examples of both spam and non-spam emails. This dataset will be used to train the machine learning model to classify emails.4. Choose a machine learning model: Choose a suitable machine learning algorithm such as Naive Bayes, Support Vector Machines, or Random Forests to classify the emails.5. Train the model: Train the machine learning model with the training dataset, so that it learns how to classify emails based on their relevant features.6. Test the model: Test the model with a separate labeled test dataset to evaluate its performance. Fine-tune the model if necessary.7. Classify new emails: Use the trained model to classify new incoming emails into spam or not-spam. The classification result can be used to move the email into the relevant folder, such as inbox or spam.8. Continuous improvement: Monitor the classification performance of the model, and update the training dataset with new examples of spam and non-spam emails to continuously improve the models classification accuracy. . Response in Sesotho: Mokhoa o ka khonehang oa ho arola li-imeile ho li-spam le eseng spam e ka kenyelletsa mehato e latelang: 1. Ntša likarolo tse amehang: Ntša likarolo tse amehang ho tsoa ho lengolo-tsoibila tse ka thusang ho hlophisoa joalo ka aterese ea motho ea e rometseng, sehlooho, mele oa lengolo-tsoibila, lihokelo, le lihokelo. 2. Preprocess ya data: Etsa mongolo preprocessing mekhoa e kang tokenization, stop-word tlosa, le stemming ho email mele le sehlooho se reng ho fumana e hloekileng ya data. 3. Theha pokello ea boitsebiso ba lithupelo: Theha pokello ea boitsebiso e ngotsoeng e nang le mehlala ea li-imeile tsa spam le tseo e seng tsa spam. Lethathamo lena la boitsebiso le tla sebelisoa ho koetlisa mokhoa oa ho ithuta oa mochine ho hlophisa mangolo-tsoibila. 4. Khetha mokhoa oa ho ithuta mochine: Khetha algorithm e loketseng ea ho ithuta mochine e kang Naive Bayes, Support Vector Machines, kapa Random Forests ho arola mangolo-tsoibila. 5. Koetlisa mohlala: Koetlisa mokhoa oa ho ithuta oa mochine ka pokello ea boitsebiso ba koetliso, e le hore o ithute ho arola mangolo-tsoibila ho latela likarolo tsa ona tse amehang. 6. Lekola mohlala: Lekola mohlala ka pokello ea lintlha tsa tlhahlobo tse ngolisitsoeng ka thoko ho lekola tšebetso ea eona. Beha mohlala ha ho hlokahala. 7. Hlophisa li-imeile tse ncha: Sebelisa mofuta o koetlisitsoeng ho arola li-imeile tse ncha tse kenang ho ba spam kapa eseng spam. Sephetho sa lihlopha se ka sebelisoa ho tsamaisa lengolo-tsoibila ho sephutheli se amehang, joalo ka inbox kapa spam. 8. Ntlafatso e tsoelang pele: Lekola tsebetso ea lihlopha tsa mohlala, me u ntlafatse lethathamo la boitsebiso ba koetliso ka mehlala e mecha ea li-imeile tsa spam le tse sa spam ho tsoela pele ho ntlafatsa ho nepahala ha lihlopha tsa mohlala. 