<prompt> Rala algorithm ea ho ithuta mochini bakeng sa ho bolela esale pele theko ea ntlo.nan  <response>: Instruction in English: Design a machine learning algorithm for predicting the price of a house. . Response in English: The task of predicting the price of a house can be approached through a supervised machine learning algorithm. Here is a high-level overview of how a machine learning algorithm that predicts the price of a house can be designed:1. Data collection and preprocessing: Collect a large dataset of information on houses and their prices. This dataset should be cleaned and preprocessed, which includes removing any incomplete or irrelevant data points, handling missing values, and standardizing the data.2. Feature selection: Identify the features or variables in the dataset that are most relevant to predicting the price of a house. These could include the location, size, number of bedrooms and bathrooms, and age of the house, among others.3. Model selection: Choose an appropriate machine learning model for the task. Some common models used for regression tasks, such as predicting a continuous value like the price of a house, include linear regression, decision trees, and neural networks.4. Training and validation: Divide the dataset into a training and validation set. Train the chosen model on the training set and assess its performance on the validation set. If the model is not performing well, adjust its parameters or try a different model.5. Evaluation: Once a satisfactory model is chosen, it can be evaluated on a test set, which should be separate from the training and validation sets, to assess its real-world performance.6. Deployment: Once the model has been trained and evaluated, it can be deployed in a production environment to make predictions on new data points.This is just a high-level overview of how a machine learning algorithm for predicting the price of a house can be designed. The specific details of the algorithm, such as the type of model used and the features selected, may vary depending on the specifics of the dataset and the problem at hand. . Response in Sesotho: Mosebetsi oa ho bolela esale pele theko ea ntlo o ka atameloa ka algorithm ea ho ithuta mochini o hlokometsoeng. Mona ke kakaretso ea boemo bo phahameng ba hore na algorithm ea ho ithuta mochini e bolelang esale pele theko ea ntlo e ka etsoa joang: 1. Pokello ea data le ho e lokisa esale pele: Bokella dataset e kholo ea tlhahisoleseling mabapi le matlo le litheko tsa ona. Lethathamo lena la data le lokela ho hloekisoa le ho sebetsoa esale pele, ho kenyelletsang ho tlosa lintlha tse sa fellang kapa tse sa sebetseng, ho sebetsana le litekanyetso tse sieo, le ho tiisa data. 2. Khetho ea likarolo: Hlalosa likarolo kapa lintho tse fapaneng lethathamong la data tse bohlokoa haholo ho lepa theko ea ntlo. Tsena li ka kenyelletsa sebaka, boholo, palo ea likamore le likamore tsa ho hlapela, lilemo tsa ntlo, hara tse ling. 3. Khetho ea mohlala: Khetha mokhoa o nepahetseng oa ho ithuta oa mochine bakeng sa mosebetsi. Mefuta e meng e tloaelehileng e sebelisoang bakeng sa mesebetsi ea ho khutlela morao, joalo ka ho bolela boleng bo tsoelang pele joalo ka theko ea ntlo, e kenyelletsa ho fokotseha ha mela, lifate tsa liqeto, le marang-rang a methapo ea kutlo. 4. Koetliso le netefatso: Arola dataset ka sete ea koetliso le netefatso. Koetlisa mohlala o khethiloeng holima sete ea koetliso me u hlahlobe tsebetso ea eona ho sete ea netefatso. Haeba mohlala o sa sebetse hantle, fetola liparamente tsa oona kapa u leke mohlala o fapaneng. 5. Tlhahlobo: Hang ha mohlala o khotsofatsang o khethiloe, o ka hlahlojoa sethaleng sa teko, se lokelang ho arohana le lihlopha tsa koetliso le ho netefatsa, ho hlahloba tsebetso ea eona ea sebele ea lefatše. 6. Ho tsamaisoa: Hang ha mohlala o se o koetlisitsoe le ho hlahlojoa, o ka sebelisoa sebakeng sa tlhahiso ho etsa likhakanyo ka lintlha tse ncha tsa data. Ena ke kakaretso ea boemo bo holimo ea hore na algorithm ea ho ithuta mochini bakeng sa ho bolela esale pele theko ea ntlo e ka etsoa joang. Lintlha tse tobileng tsa algorithm, joalo ka mofuta oa mohlala o sebelisitsoeng le likarolo tse khethiloeng, li ka fapana ho latela lintlha tse tobileng tsa dataset le bothata bo teng. 