<prompt> Theha mokhoa oa ho khetholla hore na lengolo-tsoibila ke spam kapa che.Sehlopha sa data sa li-imeile tse 200  <response>: Instruction in English: Develop a classification system to determine whether or not an email is spam. . Response in English: Sure. Here is a step-by-step process to develop a classification system to determine whether or not an email is spam:1. First, well pre-process the dataset by cleaning and formatting the text of each email. Well remove any unnecessary whitespaces or special characters and convert all text to lowercase.2. Then, well perform feature extraction by converting the emails to a numerical representation using techniques such as the Bag of Words model, where each word in the email is given a unique numerical ID, and its frequency in the email is used as its numeric value. 3. Next, well split the dataset into a training set 80 and a testing set 20.4. We can then train a machine learning model, such as Naive Bayes, Logistic Regression or Support Vector Machine, on the training dataset, using a supervised learning approach. During this process, the model will learn to identify characteristics of spam and non-spam emails.5. Once the model is trained, we can evaluate its performance on the testing dataset. Well calculate accuracy, precision, recall, and F1-Score to measure the performance of the model. If these metrics show satisfactory results, we can proceed to the next step. If not, we may try optimizing the model, changing the model or optimizing the feature extraction process, before repeating the evaluation step.6. Finally, well use the trained and optimized model to classify new emails as spam or non-spam.That is how we can develop a classification system to determine whether or not an email is spam. . Response in Sesotho: Ehlile. Mona ke mohato ka mohato oa ho theha mokhoa oa ho hlophisa ho fumana hore na lengolo-tsoibila ke spam kapa che: 1. Taba ea pele, re tla sebetsana le dataset esale pele ka ho hloekisa le ho fometa mongolo oa lengolo-tsoibila ka leng. Re tla tlosa likheo tse tšoeu tse sa hlokahaleng kapa litlhaku tse ikhethileng ebe re fetolela mongolo o mong ho ba litlhaku tse nyane. 2. Joale, re tla etsa tlhahiso ea likarolo ka ho fetolela mangolo-tsoibila hore e be boemeli ba linomoro re sebelisa mekhoa e kang mohlala oa Mokotla oa Mantsoe, moo lentsoe le leng le le leng ho lengolo-tsoibila le fuoang ID ea linomoro tse ikhethang, me khafetsa ea lengolo-tsoibila le sebelisoa joalo ka boleng ba eona ba linomoro. 3. Ka mora moo, re tla arola dataset hore e be setsi sa koetliso 80 le sete ea teko 20. 4. Joale re ka koetlisa mohlala oa ho ithuta oa mochine, o kang Naive Bayes, Logistic Regression kapa Support Vector Machine, ho dataset ea koetliso, ho sebelisa mokhoa oa ho ithuta o hlokometsoeng. Nakong ea tsebetso ena, mohlala o tla ithuta ho khetholla litšobotsi tsa li-imeile tsa spam le tseo e seng tsa spam. 5. Hang ha mohlala o koetlisitsoe, re ka hlahloba tsebetso ea oona ho dataset ea teko. Re tla bala ho nepahala, ho nepahala, ho hopola, le F1-Score ho lekanya tsebetso ea mohlala. Haeba lipalo tsena li hlahisa liphetho tse khotsofatsang, re ka fetela mohatong o latelang. Haeba ho se joalo, re ka leka ho ntlafatsa mohlala, ho fetola mohlala kapa ho ntlafatsa tsebetso ea tsebetso, pele re pheta mohato oa tlhahlobo. 6. Qetellong, re tla sebelisa mokhoa o koetlisitsoeng le o ntlafalitsoeng ho hlophisa li-imeile tse ncha e le li-spam kapa tseo e seng tsa spam. Ke ka moo re ka hlahisang mokhoa oa ho khetholla hore na imeile ke spam kapa che. 