<prompt> Etsa mokhoa oa ho ithuta ka mochini ho bona li-imeile tsa spam.nan  <response>: Instruction in English: Design a machine learning model to detect spam emails. . Response in English: To design a machine learning model for detecting spam emails, you will need to follow these steps:1. Data Collection: The first step in designing a machine learning model is to collect and prepare data. For this model, you will need to gather a data set of emails, both spam and non-spam. This dataset should be large enough to provide an accurate representation of real-world emails, and it should be divided into a training set and a testing set.2. Preprocessing: Once the data has been collected, it needs to be cleaned and preprocessed. This means removing any irrelevant information, such as email headers, and converting text to a numerical format that can be used as input for the machine learning algorithm. This can be done using techniques like tokenization and bag-of-words representation.3. Feature Selection: After the data has been preprocessed, you will need to select the most relevant features to use in your model. These might include things like the frequency of certain words or phrases, the presence of specific characters or patterns, or even the senders email address. The goal is to identify the characteristics that are most indicative of a spam email.4. Model Selection: Once you have selected the relevant features, you will need to choose a machine learning algorithm to use for your model. There are many different algorithms to choose from, including decision trees, naive bayes, logistic regression, and support vector machines. The best algorithm for your model will depend on your specific data set and the features you have chosen.5. Training: After selecting an algorithm, the next step is to train your model using the training data. This involves feeding the data into the algorithm and adjusting the models parameters to minimize the error between the predicted and actual labels.6. Evaluation: Once the model has been trained, it needs to be evaluated using the testing data. This will give you an idea of how well the model generalizes to new data and how accurate its predictions are. The evaluation metrics you use will depend on your specific goals, but common options include accuracy, precision, recall, and f-score.7. Optimization: If the evaluation results show that the model can be improved, the next step is to optimize it. This can be done by adjusting the models parameters, selecting different features, or even trying a different algorithm altogether.Overall, designing a spam email detection model is a complex process that involves many different steps. By following these steps . Response in Sesotho: Ho rala mokhoa oa ho ithuta ka mochini bakeng sa ho bona li-imeile tsa spam, o tla hloka ho latela mehato ena: 1. Pokello ea Lintlha: Mohato oa pele oa ho rala mokhoa oa ho ithuta mochini ke ho bokella le ho lokisa lintlha. Bakeng sa mofuta ona, o tla hloka ho bokella lethathamo la li-imeile, ka bobeli li-spam le tseo e seng tsa spam. Setlankana sena sa boitsebiso se lokela ho ba seholo ho lekana ho fana ka tlhahiso e nepahetseng ea li-imeile tsa lefatše la sebele, me se lokela ho aroloa ka sete ea koetliso le sehlopha sa liteko. 2. Ho sebetsa esale pele: Hang ha data e se e bokelletsoe, e hloka ho hloekisoa le ho sebetsoa esale pele. Sena se bolela ho tlosa tlhahisoleseling efe kapa efe e sa lokelang, joalo ka lihlooho tsa lengolo-tsoibila, le ho fetolela mongolo mofuteng oa linomoro o ka sebelisoang e le kenyelletso bakeng sa algorithm ea ho ithuta mochini. Sena se ka etsoa ho sebelisoa mekhoa e kang tokenization le boemeli ba mokotla oa mantsoe. 3. Khetho ea Litšobotsi: Ka mora hore data e hlophisitsoe esale pele, u tla hloka ho khetha likarolo tse bohlokoa haholo tseo u ka li sebelisang moetsong oa hau. Sena se ka kenyelletsa lintho tse kang khafetsa mantsoe kapa lipoleloana tse itseng, ho ba teng ha litlhaku tse itseng kapa lipaterone, kapa aterese ea lengolo-tsoibila la motho ea e rometseng. Sepheo ke ho khetholla litšobotsi tse bontšang haholo ka imeile ea spam. 4. Khetho ea Moetso: Hang ha u se u khethile likarolo tse amehang, u tla hloka ho khetha algorithm ea ho ithuta ka mochini eo u ka e sebelisang molemong oa hau. Ho na le li-algorithms tse ngata tse fapaneng tseo u ka khethang ho tsona, ho kenyeletsoa lifate tsa liqeto, li-bayes tse sa tsebeng letho, li-logistic regression, le mechini ea vector ea tšehetso. Algorithm e ntle ka ho fetisisa ea mohlala oa hau e tla itšetleha ka sete ea hau ea data le likarolo tseo u li khethileng. 5. Koetliso: Kamora ho khetha algorithm, mohato o latelang ke ho koetlisa mohlala oa hau u sebelisa lintlha tsa koetliso. Sena se kenyelletsa ho fepa data ho algorithm le ho lokisa liparamente tsa mohlala ho fokotsa phoso lipakeng tsa lileibole tse boletsoeng esale pele le tsa nete. 6. Tlhahlobo: Hang ha mohlala o se o koetlisoa, o hloka ho hlahlojoa ho sebelisoa lintlha tsa tlhahlobo. Sena se tla u fa leseli la hore na mohlala o akaretsa hantle hakae ho data e ncha le hore na likhakanyo tsa eona li nepahetse hakae. Litekanyetso tsa tlhahlobo tseo u li sebelisang li tla ipapisa le sepheo sa hau, empa likhetho tse tloaelehileng li kenyelletsa ho nepahala, ho nepahala, ho hopola, le f-score. 7. Katoloso: Haeba liphetho tsa tlhahlobo li bontša hore mohlala o ka ntlafatsoa, mohato o latelang ke ho o ntlafatsa. Sena se ka etsoa ka ho lokisa liparamente tsa mohlala, ho khetha likarolo tse fapaneng, kapa esita le ho leka algorithm e fapaneng ka ho felletseng. Ka kakaretso, ho etsa mohlala oa ho fumana lengolo-tsoibila la spam ke tsebetso e rarahaneng e kenyelletsang mehato e mengata e fapaneng. Ka ho latela mehato ena 