<prompt> Etsa mokhoa oa ho ithuta oa mochini ho hlophisa li-imeile joalo ka spam kapa eseng spam.NA  <response>: Instruction in English: Design a machine learning model to classify emails as spam or not spam. . Response in English: To design a machine learning model to classify emails as spam or not spam, we can follow these steps:1. Data Collection: Gather a large dataset of emails labeled as spam or not spam. This data can be collected from various sources such as email providers, public datasets or by manually labeling a set of emails.2. Preprocessing: Clean the data by removing any irrelevant information, fixing any incorrect data, handling missing values, and converting data into a machine-readable format. In the case of emails, preprocessing may involve text cleaning, removing stop words and special characters, and tokenizing the text. 3. Feature Extraction: Extract features from the preprocessed data that can be useful in identifying spam emails. This can be done by using techniques such as Bag of Words or Term Frequency-Inverse Document Frequency TF-IDF to convert the text data into numerical values. Additional features such as the length of the email, sender information, and time of the email can also be helpful in distinguishing spam emails.4. Model Selection: Choose a suitable machine learning algorithm to classify the emails as spam or not spam. Some popular algorithms for email classification are Naive Bayes, Support Vector Machines, and Decision Trees.5. Training: Divide the dataset into a training and testing set. Train the selected model on the training set by feeding it the extracted features and their corresponding labels.6. Evaluation: Evaluate the performance of the model on the testing set by calculating metrics such as accuracy, precision, recall, and F1-score. If the performance is not satisfactory, experiment with different algorithms, features or hyperparameters to improve the model.7. Deployment: Once a satisfactory model is obtained, it can be deployed to classify incoming emails as spam or not spam. The model can be integrated with an email service to automatically move spam emails to a separate folder, or to flag them for the users attention.Finally, it is important to regularly update the model with new data to ensure that it remains accurate and is able to adapt to changing patterns of spam emails. . Response in Sesotho: Ho rala mokhoa oa ho ithuta ka mochini ho khetholla li-imeile joalo ka spam kapa eseng spam, re ka latela mehato ena: 1. Pokello ea Lintlha: Bokella pokello ea data e kholo ea li-imeile tse ngotsoeng joalo ka spam kapa eseng spam. Lintlha tsena li ka bokelloa mehloling e fapaneng joalo ka bafani ba mangolo-tsoibila, li-database tsa sechaba kapa ka ho ngola li-imeile ka bowena. 2. Ho lokisa esale pele: Hloekisa boitsebiso ka ho tlosa tlhahisoleseding leha e le efe e sa lokelang, ho lokisa lintlha leha e le life tse fosahetseng, ho sebetsana le litekanyetso tse sieo, le ho fetola boitsebiso ka mokhoa o ka baloang ka mochine. Tabeng ea mangolo-tsoibila, ho sebetsa esale pele ho ka kenyelletsa ho hloekisa mongolo, ho tlosa mantsoe a emisang le litlhaku tse ikhethileng, le ho etsa tokening mongolo. 3. Feature Extraction: Ntša likarolo ho data e seng e hlophisitsoe tse ka thusang ho tsebahatsa li-imeile tsa spam. Sena se ka etsoa ka ho sebelisa mekhoa e kang Mokotla oa Mantsoe kapa Term Frequency-Inverse Document Frequency TF-IDF ho fetolela lintlha tsa mongolo ho ba boleng ba linomoro. Likarolo tse ling tse joalo ka bolelele ba lengolo-tsoibila, lintlha tsa motho ea rometseng, le nako ea lengolo-tsoibila le tsona li ka thusa ho khetholla li-imeile tsa spam. 4. Khetho ea Mohlala: Khetha algorithm e loketseng ea ho ithuta mochini ho hlophisa li-imeile joalo ka spam kapa eseng spam. Li-algorithms tse ling tse tsebahalang tsa ho arola mangolo-tsoibila ke Naive Bayes, Mechini ea Tsehetso ea Vector, le Lifate tsa Liqeto. 5. Koetliso: Arola dataset hore e be setsi sa koetliso le tlhahlobo. Koetlisa mohlala o khethiloeng thupelong e behiloeng ka ho e fepa likarolo tse nkiloeng le li-label tsa tsona tse lumellanang. 6. Tlhahlobo: Lekola tsebetso ea mohlala tekong e behiloeng ka ho bala metrics e kang ho nepahala, ho nepahala, ho hopola, le F1-score. Haeba tsebetso e sa khotsofatse, leka mekhoa e fapaneng, likarolo kapa hyperparameter ho ntlafatsa mohlala. 7. Tšebeliso: Hang ha mohlala o khotsofatsang o fumanoa, o ka fetisetsoa ho arola li-imeile tse kenang e le spam kapa eseng spam. Moetso ona o ka kopanngoa le tšebeletso ea lengolo-tsoibila ho tsamaisa li-imeile tsa spam ho foldara e arohaneng, kapa ho li tšoaea bakeng sa tlhokomelo ea mosebelisi. Qetellong, ke habohlokoa ho ntlafatsa kamehla mohlala ka lintlha tse ncha ho netefatsa hore o lula o nepahetse me o khona ho ikamahanya le maemo a fetohang a li-imeile tsa spam. 