<prompt> Bolela mekhoa e 3 e ka sebelisoang ho hloekisa data.nan  <response>: Instruction in English: Name 3 methods that can be used for data cleaning. . Response in English: 1. Handling Missing Data: One of the methods for data cleaning is handling missing data by either removing the entire record or filling in the missing values with an appropriate method, such as mean imputation, regression imputation, or multiple imputations. 2. Removing Duplicates: Another method for data cleaning is the removal of duplicate values. This can be done by checking for identical rows and deleting the extra copies or by using algorithms to identify and remove fuzzy duplicates.3. Outlier Detection Treatment: Outlier detection is used to identify and treat data points that are significantly different from the rest of the data. These outliers can be treated by removing them or by replacing them with more realistic values. This can be done using statistical techniques like Z-score, IQR method, or by visualizing the data through box plots or scatter plots. . Response in Sesotho: 1. Ho sebetsana le Lintlha tse Sieo: E ngoe ea mekhoa ea ho hloekisa data ke ho sebetsana le lintlha tse sieo ka ho tlosa rekoto eohle kapa ho tlatsa litekanyetso tse sieo ka mokhoa o nepahetseng, joalo ka ho qosoa ka moelelo, ho khutlisetsoa morao, kapa ho pheta-pheta ka makhetlo a mangata. 2. Ho Tlosa Likopi: Mokhoa o mong oa ho hloekisa data ke ho tlosoa ha litekanyetso tse kopitsoang. Sena se ka etsoa ka ho sheba mela e tsoanang le ho hlakola likopi tse ling kapa ka ho sebelisa li-algorithms ho tseba le ho tlosa likopi tse sa hlakang. 3. Ho Hlahlobisisa ka Ntle le Kalafo: Ho lemoha ka ntle ho sebelisoa ho khetholla le ho tšoara lintlha tsa data tse fapaneng haholo le tse ling kaofela. Lintho tsena tse ka ntle li ka phekoloa ka ho li tlosa kapa ka ho li nkela sebaka ka litekanyetso tsa sebele. Sena se ka etsoa ho sebelisoa mekhoa ea lipalo-palo joalo ka Z-score, mokhoa oa IQR, kapa ka ho bona lintlha ka mahlohonolo a mapolanka kapa mapolanka a hasanyang. 