<prompt> Theha mofuta oa lihlopha o ka khethollang hore na lengolo-tsoibila le fanoeng ke spam kapa che.Ha ho tlhahiso  <response>: Instruction in English: Create a classification model which can classify whether a given email is spam or not. . Response in English: To create a classification model that can classify whether a given email is spam or not, we can follow these steps:1. Collect and analyze data: The first step would be to gather a large dataset of emails that have already been classified as either spam or not. This data will be used to train our classification model. We can analyze the data to identify the characteristics of spam emails, such as certain keywords or patterns.2. Preprocess the data: Before we begin building the model, we need to clean and preprocess the data. This includes removing any irrelevant information, correcting mistakes, and transforming the data into a format that can be fed into a machine learning algorithm.3. Select a machine learning algorithm: There is a wide range of machine learning algorithms that can be used for email spam classification, including Naive Bayes, Support Vector Machines SVM, and Decision Trees. We need to choose the algorithm that best fits our data and can provide accurate results.4. Train the model: Once the data has been preprocessed and the algorithm has been selected, we can train our model by feeding it the training data. The model will learn from the data and adjust its parameters to accurately classify spam emails.5. Evaluate the model: After the model has been trained, we need to evaluate its performance by testing it on a separate set of data that was not used during training. We can use metrics such as accuracy, precision, and recall to measure the models performance.6. Fine-tune the model: If the models performance is not satisfactory, we can fine-tune it by adjusting its parameters or selecting a different algorithm.7. Deploy the model: Once the model is fine-tuned and is providing accurate results, it can be deployed to classify emails as spam or not. The model can be integrated into an email client or server to automatically filter out spam emails. . Response in Sesotho: Ho theha mofuta oa lihlopha o ka khethollang hore na lengolo-tsoibila le fanoeng ke spam kapa che, re ka latela mehato ena: 1. Bokella le ho sekaseka lintlha: Mohato oa pele e tla ba ho bokella lethathamo le leholo la li-imeile tse seng li arotsoe joalo ka spam. kapa eseng joalo. Lintlha tsena li tla sebelisoa ho koetlisa mofuta oa rona oa lihlopha. Re ka sekaseka lintlha ho tseba litšobotsi tsa li-imeile tsa spam, joalo ka mantsoe a mang a bohlokoa kapa lipaterone. 2. Hlakola data esale pele: Pele re qala ho aha mohlala, re hloka ho hloekisa le ho lokisa data esale pele. Sena se kenyelletsa ho tlosa tlhahisoleseling efe kapa efe e sa lokelang, ho lokisa liphoso, le ho fetolela data ho sebopeho se ka feptjoang ho algorithm ea ho ithuta mochini. 3. Khetha algorithm ea ho ithuta mochine: Ho na le mekhoa e mengata ea ho ithuta mochine e ka sebelisoang bakeng sa ho arola melaetsa ea spam, ho akarelletsa le Naive Bayes, Support Vector Machines SVM, le Decision Trees. Re hloka ho khetha algorithm e lumellanang hantle le data ea rona mme e ka fana ka liphetho tse nepahetseng. 4. Koetlisa mohlala: Hang ha data e se e hlophisitsoe esale pele me algorithm e khethiloe, re ka koetlisa mohlala oa rona ka ho o fepa lintlha tsa koetliso. Moetso o tla ithuta ho tsoa ho data mme o fetole maemo a ona ho hlophisa ka nepo li-imeile tsa spam. 5. Hlahloba mohlala: Ka mora hore mohlala o koetlisoe, re lokela ho hlahloba tsebetso ea oona ka ho o hlahloba ka sete e fapaneng ea data e sa kang ea sebelisoa nakong ea koetliso. Re ka sebelisa metrics joalo ka ho nepahala, ho nepahala, le ho hopola ho lekanya tsebetso ea mohlala. 6. Beakanya mohlala: Haeba tsebetso ea mohlala e sa khotsofatse, re ka e lokisa hantle ka ho fetola liparamente tsa eona kapa ho khetha algorithm e fapaneng. 7. Sebelisa mohlala: Hang ha mohlala o hlophisitsoe hantle me o fana ka liphetho tse nepahetseng, o ka sebelisoa ho arola li-imeile e le spam kapa che. Moetso o ka kopanngoa ho moreki oa lengolo-tsoibila kapa seva ho sefa li-imeile tsa spam ka bohona. 