<prompt> Theha algorithm bakeng sa ho bolela esale pele hore na lengolo-tsoibila ke spam kapa che.nan  <response>: Instruction in English: Develop an algorithm for predicting whether an email is spam or not. . Response in English: One approach to developing an algorithm for predicting whether an email is spam or not is as follows:1. Data collection: The first step in building a spam filter is to collect a large dataset of emails, labeled as either spam or non-spam ham.2. Data preprocessing: This stage involves cleaning and preparing the data for the algorithm. Emails are converted to lowercase, and all special characters and numbers are removed. Stop words are also removed.3. Feature extraction: The next step is to extract features from emails that can be used to make predictions. One approach is to use the bag-of-words model, where the presence of certain pre-defined words in an email can be used to predict spam. For instance, words such as offer, free, click, and buy can signal that an email is spam.4. Model training: In this stage, a machine learning algorithm, such as Naive Bayes, Logistic Regression, or Decision Tree is used to train the model using the pre-processed data.5. Cross-validation: Cross-validation is used to measure the accuracy of the model. This step involves splitting the dataset into training and validation data, then using the training data to train the model and the validation data to measure its performance.6. Model evaluation: The model is evaluated using various metrics such as accuracy, precision, and recall. The goal is to maximize the true positive rate, while minimizing the false positive rate.7. Deployment: Once the model has been trained and validated, it is deployed and used to predict whether incoming emails are spam or not.In summary, the algorithm involves collecting and pre-processing data, extracting relevant features, training and validating a machine learning model, and finally deploying the model to make predictions. . Response in Sesotho: Mokhoa o mong oa ho etsa algorithm bakeng sa ho bolela esale pele hore na lengolo-tsoibila ke spam kapa che ke ka tsela e latelang: 1. Pokello ea lintlha: Mohato oa pele oa ho haha sefa sa spam ke ho bokella lethathamo le leholo la li-imeile, tse ngotsoeng e le spam kapa e seng spam hemo. 2. Phatlalatso ea data: Mokhahlelo ona o kenyelletsa ho hloekisa le ho lokisa lintlha tsa algorithm. Li-imeile li fetoloa litlhaku tse nyane, me litlhaku tsohle tse khethehileng le linomoro lia tlosoa. Mantsoe a ho emisa le ona a tlosoa. 3. Tlhahiso ea likarolo: Mohato o latelang ke ho ntša likarolo ho tsoa ho li-imeile tse ka sebelisoang ho etsa likhakanyo. Mokhoa o mong ke oa ho sebelisa mohlala oa mokotla oa mantsoe, moo boteng ba mantsoe a itseng a boletsoeng esale pele ka lengolo-tsoibila le ka sebelisoang ho bolela esale pele spam. Mohlala, mantsoe a kang offer, mahala, tobetsa, le reka a ka bontša hore lengolo-tsoibila ke spam. 4. Koetliso ea mohlala: Mothating ona, algorithm ea ho ithuta mochine, e kang Naive Bayes, Logistic Regression, kapa Decision Tree e sebelisetsoa ho koetlisa mohlala ho sebelisa lintlha tse entsoeng esale pele. 5. Ho netefatsoa ka sefapano: Ho sebelisoa ho netefatsoa ka sefapano ho lekanya ho nepahala ha mohlala. Mohato ona o kenyelletsa ho arola dataset ho data ea koetliso le ho netefatsa, ebe o sebelisa boitsebiso ba koetliso ho koetlisa mohlala le boitsebiso ba ho netefatsa ho lekanya tshebetso ea eona. 6. Tlhahlobo ea mohlala: Moetso o lekoa ho sebelisoa metrics e fapaneng joalo ka ho nepahala, ho nepahala, le ho hopola. Sepheo ke ho eketsa tekanyo e nepahetseng ea nete, ha u ntse u fokotsa sekhahla se fosahetseng. 7. Phatlalatso: Hang ha mohlala o se o koetlisitsoe le ho netefatsoa, o sebelisoa mme o sebelisetsoa ho bolela esale pele hore na li-imeile tse kenang ke spam kapa che. Ka bokhutšoanyane, algorithm e kenyelletsa ho bokella le ho sebetsa pele ho data, ho ntša likarolo tse loketseng, ho koetlisa le ho netefatsa mokhoa oa ho ithuta mochine, me qetellong ho sebelisa mohlala ho etsa likhakanyo. 