<prompt> Theha mofuta oa maemo o bolelang esale pele hore na tlhahlobo e fanoeng e mabapi le sehlahisoa kapa tšebeletsonan  <response>: Instruction in English: Create a classfication model that predicts whether a given review is about a product or a service . Response in English: One approach you could take to build this model is to use a machine learning pipeline that includes text preprocessing, feature extraction, and classification. Here is an example implementation:1. Data collection: Collect a set of review texts that have been labeled as either Product or Service. You can collect this data by scraping customer review sites, or by using a pre-existing dataset.2. Text preprocessing: Clean the review texts in preparation for feature extraction. This step may include removing punctuation, converting all characters to lowercase, tokenizing the text into individual words, and removing stop words.3. Feature extraction: Use techniques like bag-of-words or TF-IDF to convert the text data into numerical feature vectors that can be used in machine learning algorithms.4. Model training: Choose a classification algorithm like logistic regression, decision tree, or support vector machine to train on your feature vectors and target labels.5. Model evaluation: Evaluate the performance of the model using metrics like accuracy, precision, recall, and F1-score. You may want to use techniques like cross-validation to avoid overfitting.6. Prediction: Once the model is trained and evaluated, you can use it to predict whether a new review is about a product or a service by converting the review text into a feature vector using the same feature extraction method used during training, and then passing the vector to the models predict method. . Response in Sesotho: Mokhoa o mong oo u ka o nkang ho aha mofuta ona ke ho sebelisa pompo ea ho ithuta ka mochini e kenyelletsang ho hlophisoa esale pele, ho ntšoa ha likarolo, le ho arola. Mohlala oa tsebetsong ke ona: 1. Pokello ea lintlha: Bokella sehlopha sa litemana tsa tlhahlobo tse ngotsoeng e le Sehlahisoa kapa Tšebeletso. U ka bokella lintlha tsena ka ho hlakola libaka tsa tlhahlobo ea bareki, kapa ka ho sebelisa dataset e seng e ntse e le teng. 2. Tlhahiso pele ea mongolo: Hloekisa litemana tsa tlhahlobo ho itokisetsa ho ntša likarolo. Mohato ona o ka kenyelletsa ho tlosa matšoao a puo, ho fetolela litlhaku tsohle ho litlhaku tse nyane, ho fetola mongolo hore e be mantsoe a ikhethileng, le ho tlosa mantsoe a emisang. 3. Ntsollo ea likarolo: Sebelisa mekhoa e kang mokotla-oa-mantsoe kapa TF-IDF ho fetolela lintlha tsa mongolo hore e be li-vector tsa likarolo tsa linomoro tse ka sebelisoang ho algorithms ea ho ithuta ka mochini. 4. Koetliso ea mohlala: Khetha algorithm ea ho hlopha joalo ka ho theoha ha lintho, sefate sa liqeto, kapa mochini o tšehetsang vector ho koetlisa li-vector tsa likarolo tsa hau le lileibole tseo u li batlang. 5. Tlhahlobo ea mohlala: Lekola tsebetso ea mohlala u sebelisa metrics joalo ka ho nepahala, ho nepahala, ho hopola, le lintlha tsa F1. U ka na ua batla ho sebelisa mekhoa e kang cross-validation ho qoba overfitting. 6. Prediction: Hang ha mmotlolo o se o kwetliseditswe le ho hlahlojwa, o ka o sebedisa ho lepa hore na tlhahlobo e ntjha e mabapi le sehlahiswa kapa tshebeletso ka ho fetolela sengoloa sa tekolo hore e be vector ya sebopeho o sebedisa mokgwa o tshwanang wa ho qotsa o sebediswang nakong. koetliso, ebe o fetisetsa vector ho mokhoa oa ho bolela esale pele oa mohlala. 