<prompt> Etsa tlhahiso ea moralo oa ho hlahisa mohlala oa li-analytics tsa ho bolela esale pele.nan  <response>: Instruction in English: Suggest a framework for developing a predictive analytics model. . Response in English: Developing a predictive analytics model involves several steps organized in a framework. Here is a suggested framework that one could use:1. Define the problem: Start by identifying the business problem and defining the objectives of the model you want to develop. Define the outcomes that you want to predict, as well as the inputs or predictor variables you will use.2. Collect and prepare data: You need to collect high-quality data to train and test your models. Once you have the data, you need to preprocess it to clean, format, and structure it for analysis. This may include handling missing data, transforming variables, encoding categorical variables, and normalizing data.3. Explore and visualize data: This step involves performing exploratory data analysis to understand the data you are working with. This includes summarizing the data with descriptive statistics, visualizing data distributions, and identifying correlations and relationships between variables.4. Select variables: You need to identify which variables to use in your model. This may involve selecting a subset of variables based on their correlation with the outcome, using statistical tests to identify significant variables, or using feature selection algorithms.5. Choose a modeling approach: Select a suitable modeling approach based on the objectives, data, and problem you are trying to solve. This may involve choosing between supervised or unsupervised learning, selecting a specific algorithm such as linear regression, decision trees, or neural networks, and defining the model parameters.6. Train and test the model: Split your data into training and test sets, and use the training set to build and train your model. Then, test the model on the test set to evaluate its performance. You may need to iteratively refine and retrain the model, and tune the parameters until the model reaches a satisfactory level of performance.7. Deploy the model: Once your model is trained and tested, you can deploy it to start making predictions on new data. You may need to integrate the model into existing systems or processes, and define a method for updating the model as new data becomes available.8. Monitor and evaluate the model: Finally, you need to monitor the models performance, ensure it is meeting the objectives, and evaluate its effectiveness over time. You may need to maintain, update or retrain the model to ensure it remains relevant and accurate.By following this framework, you can develop a robust predictive analytics model that effectively meets the objectives and provides accurate and reliable predictions. . Response in Sesotho: Ho theha mofuta oa analytics oa ho bolela esale pele ho kenyelletsa mehato e mengata e hlophisitsoeng ka moralo. Mona ke moralo o khothalelitsoeng oo motho a ka o sebelisang: 1. Hlalosa bothata: Qala ka ho hloaea bothata ba khoebo le ho hlalosa sepheo sa mohlala oo u batlang ho o hlahisa. Hlalosa liphetho tseo u batlang ho li bolela esale pele, hammoho le lintho tse kenang kapa tse fapaneng tseo u tla li sebelisa. 2. Bokella le ho lokisa lintlha: U hloka ho bokella lintlha tsa boleng bo holimo ho koetlisa le ho leka mehlala ea hau. Ha u se u e-na le data, u lokela ho e lokisa esale pele ho e hloekisa, ho e fomata le ho e hlophisa bakeng sa tlhahlobo. Sena se ka kenyelletsa ho sebetsana le data e sieo, ho fetola mefuta e fapaneng, ho kenyelletsa mefuta e fapaneng ea likarolo, le data e tloaelehileng. 3. Hlahloba le ho bona lintlha ka mahlo a kelello: Mohato ona o kenyelletsa ho etsa tlhahlobo ea lintlha tsa lipatlisiso ho utloisisa lintlha tseo u sebetsang le tsona. Sena se kenyelletsa ho akaretsa lintlha ka lipalo-palo tse hlalosang, ho bona kabo ea data, le ho supa likamano le likamano lipakeng tsa mefuta e fapaneng. 4. Khetha mefuta-futa: U lokela ho tseba hore na ke mefuta efe eo u ka e sebelisang moetsong oa hau. Sena se ka na sa kenyelletsa ho khetha sehlopha se senyenyane sa mefuta-futa ho latela kamano ea tsona le sephetho, ho sebelisa liteko tsa lipalo-palo ho fumana mefuta e mengata ea bohlokoa, kapa ho sebelisa mekhoa ea ho khetha likarolo. 5. Khetha mokhoa oa ho etsa mohlala: Khetha mokhoa o loketseng oa ho etsa mohlala o thehiloeng ho sepheo, lintlha, le bothata boo u lekang ho bo rarolla. Sena se ka kenyelletsa ho khetha lipakeng tsa thuto e hlokometsoeng kapa e sa laoloang, ho khetha algorithm e itseng joalo ka ho fokotseha ha mela, lifate tsa liqeto, kapa marang-rang a methapo, le ho hlalosa liparamente tsa mohlala. 6. Koetlisa le ho leka mohlala: Arola lintlha tsa hau ho lithupelo le lihlopha tsa liteko, me u sebelise sete ea koetliso ho haha le ho koetlisa mohlala oa hau. Ebe, leka mohlala ho sete ea teko ho lekola tsebetso ea eona. U ka na ua hloka ho ntlafatsa khafetsa le ho koetlisa mohlala hape, le ho lokisa liparamente ho fihlela mohlala o fihla boemong bo khotsofatsang ba tsebetso. 7. Tsamaisa mohlala: Hang ha mohlala oa hau o koetlisoa le ho lekoa, u ka o sebelisa ho qala ho bolela esale pele ka data e ncha. U kanna ua hloka ho kenyelletsa mohlala ho litsamaiso kapa litsebetso tse teng, me u hlalose mokhoa oa ho nchafatsa mohlala ha data e ncha e fumaneha. 8. Ela hloko le ho hlahloba mohlala: Qetellong, o hloka ho beha mohlala ho beha leihlo tšebetso ea oona, ho etsa bonnete ba hore o finyella lipheo, le ho hlahloba katleho ea oona ha nako e ntse e ea. U ka na ua hloka ho boloka, ho ntlafatsa kapa ho koetlisa mohlala ho netefatsa hore o lula o sebetsa ebile o nepahetse. Ka ho latela moralo ona, o ka hlahisa mohlala o matla oa ho bolela esale pele o fihlelang sepheo ka nepo mme o fana ka likhakanyo tse nepahetseng le tse tšepahalang. 