<prompt> Theha mokhoa oa ho lemoha ha molaetsa oa imeile e le scam.nan  <response>: Instruction in English: Create a system to detect when an email message is a scam. . Response in English: One way to detect when an email message is a scam can be by implementing a system using machine learning and natural language processing techniques. The system can be designed with the following steps:1. Data gathering: Collect a large sample of legitimate and scam emails to use as training data for the system. This data can be obtained from public datasets, or emails marked as spam by users or by email service providers.2. Preprocessing: Clean and preprocess the data to remove any unnecessary information such as email headers, signatures, etc. Convert the text of the emails into numerical representations using techniques like tokenization, stemming, and lemmatization for ease of analysis.3. Feature extraction: Extract features from the cleaned data to use as input to the machine learning model. These features could include the senders email address, the subject line, any URLs or attachments in the email, or the text of the email itself.4. Model training: Train a machine learning algorithm such as a support vector machine, a naive Bayes classifier, or a neural network on the preprocessed data using the extracted features.5. Evaluation: Evaluate the performance of the model on a separate test dataset to ensure its accuracy and robustness.6. Implementation: Implement the system by integrating it with an email client or service, so that emails can be automatically flagged as potential scams.7. Continuous improvement: Continuously monitor the performance of the system, and collect feedback from users to improve and update the model.With this system in place, incoming emails can be automatically analyzed and flagged as potential scams, helping to protect users from falling victim to fraudulent messages. . Response in Sesotho: Mokhoa o mong oa ho lemoha ha molaetsa oa lengolo-tsoibila e le scam e ka ba ka ho kenya tsebetsong sistimi e sebelisang ho ithuta ka mochini le mekhoa ea tsebetso ea puo ea tlhaho. Sistimi e ka raloa ka mehato e latelang: 1. Ho bokella lintlha: Bokella sampole e kholo ea mangolo-tsoibila a nepahetseng le a bolotsana ho a sebelisa e le lintlha tsa koetliso bakeng sa sistimi. Lintlha tsena li ka fumanoa ho tsoa ho li-database tsa sechaba, kapa li-imeile tse tšoailoeng e le spam ke basebelisi kapa ke bafani ba litšebeletso tsa imeile. 2. Preprocessing: Hloekisa me u sebedise data pele ho tlosa tlhahisoleseding efe kapa efe e sa hlokahaleng e kang lihlooho tsa imeile, li-signature, joalo-joalo Fetola mongolo oa mangolo-tsoibila hore e be lipapiso tsa linomoro u sebelisa mekhoa e kang tokenization, stemming, le lemmatization bakeng sa boiketlo ba ho hlahloba. 3. Phatlalatso ea likarolo: Ntša likarolo ho data e hloekisitsoeng ho e sebelisa e le mokhoa oa ho ithuta mochine. Likarolo tsena li ka kenyelletsa aterese ea lengolo-tsoibila, sehlooho, li-URL life kapa life kapa liphutheloana tse ho lengolo-tsoibila, kapa mongolo oa lengolo-tsoibila ka boeona. 4. Koetliso ea mohlala: Koetlisa algorithm ea ho ithuta ka mochini joalo ka mochini oa vector ea tsehetso, sehlopha sa Bayes se sa tsebeng letho, kapa marang-rang a neural ho data e sebetsitsoeng esale pele o sebelisa likarolo tse ntšitsoeng. 5. Tlhahlobo: Lekola tsebetso ea mohlala ho dataset ea tlhahlobo e arohaneng ho netefatsa ho nepahala le ho tiea ha eona. 6. Phethahatso: Kenya tšebetsong sistimi ka ho e kopanya le moreki oa lengolo-tsoibila kapa tšebeletso, e le hore li-imeile li ka tšoauoa ka bo eona e le bomenemene. 7. Ntlafatso e tsoelang pele: Tsoela pele ho shebella tsebetso ea tsamaiso, me u bokelle maikutlo a basebelisi ho ntlafatsa le ho ntlafatsa mohlala. Ha tsamaiso ena e ntse e le teng, li-imeile tse kenang li ka hlahlojoa le ho tšoauoa ka mokhoa o ikemetseng e le bolotsana bo ka bang teng, ho thusa ho sireletsa basebelisi hore ba se ke ba oela melaetsa ea bolotsana. 