<prompt> Etsa algorithm bakeng sa ho bolela esale pele litheko tsa ntlo.nan  <response>: Instruction in English: Create an algorithm for predicting house prices. . Response in English: Here is an outline of an algorithm that could be used for predicting house prices:1. Collect data: Gather data on house prices and features that may affect prices, such as location, size, number of rooms, age, and condition. This data can be obtained from public records, real estate listings or other sources.2. Preprocess data: Clean and organize the data, removing any missing or incorrect values. Transform categorical data into numerical data, if necessary.3. Split the data: Split the data into a training set and a testing set. Use the training data to build the predictive model and the testing data to evaluate its performance.4. Choose and train the model: Select a model for making predictions, such as linear regression or a decision tree. Train the model using the training data by fitting it to the data and adjusting its parameters to minimize prediction errors.5. Evaluate the model: Test the models performance by using it to make predictions on the testing data. Compare the predicted values with the actual values to assess the accuracy of the model.6. Make predictions: Once the model has been evaluated and optimized, use it to make predictions on new data. Input the features of a house into the model to obtain a predicted price.7. Update the model: Continuously collect new data and use it to update the model to ensure that it remains accurate and relevant. Re-evaluate the model periodically to assess its performance and make any necessary changes. . Response in Sesotho: Mona ke kemiso ea algorithm e ka sebelisoang ho bolela esale pele litheko tsa ntlo: 1. Bokella lintlha: Bokella lintlha ka litheko tsa ntlo le likarolo tse ka amang litheko, joalo ka sebaka, boholo, palo ea likamore, lilemo le maemo. Lintlha tsena li ka fumanoa lirekotong tsa sechaba, lethathamong la thekiso ea matlo kapa mehloling e meng. 2. Lintlha tsa Preprocess: Hloekisa le ho hlophisa lintlha, ho tlosa litekanyetso leha e le life tse sieo kapa tse fosahetseng. Fetolela lintlha tsa categorical ho data ea linomoro, ha ho hlokahala. 3. Arola lintlha: Arola lintlha ka sete ea koetliso le sete ea liteko. Sebelisa lintlha tsa koetliso ho aha mohlala oa ho bolela esale pele le lintlha tsa tlhahlobo ho lekola tsebetso ea eona. 4. Khetha me u koetlise mohlala: Khetha mohlala bakeng sa ho bolela esale pele, joalo ka ho fokotseha ha mela kapa sefate sa liqeto. Koetlisa mohlala ho sebelisa lintlha tsa koetliso ka ho li kenya ho data le ho lokisa liparamente tsa eona ho fokotsa liphoso tsa ho bolela esale pele. 5. Lekola mohlala: Lekola tsebetso ea mohlala ka ho o sebelisa ho bolela esale pele ka data ea tlhahlobo. Bapisa litekanyetso tse boletsoeng esale pele le litekanyetso tsa sebele ho hlahloba ho nepahala ha mohlala. 6. Etsa likhakanyo: Hang ha mohlala o se o hlahlobiloe le ho ntlafatsoa, o sebelise ho bolela esale pele ka data e ncha. Kenya likarolo tsa ntlo ho mohlala ho fumana theko e boletsoeng esale pele. 7. Nchafatsa mohlala: Tsoela pele ho bokella lintlha tse ncha le ho li sebelisa ho nchafatsa mohlala ho netefatsa hore o lula o nepahetse ebile o le bohlokoa. Lekola mohlala hape nako le nako ho lekola tsebetso ea ona le ho etsa liphetoho tse hlokahalang. 