<prompt> Hlalosa hore na data preprocessing ke eng, me u fane ka mohlala o le mong.nan  <response>: Instruction in English: Explain what data preprocessing is, and provide one example. . Response in English: Data preprocessing is the process of cleaning and transforming raw data into a format that is easily understandable and suitable for analysis. The aim of data preprocessing is to enhance the quality of data, making it ready for use in machine learning, data mining, and other analytic models.An example of data preprocessing is handling missing data. Often, datasets have missing values or incomplete information which may interfere with the final analysis. Data preprocessing techniques can be used to identify, handle and fill these missing data values to make the dataset more complete and usable. One technique for handling missing data is called imputation - this involves replacing the missing data with a statistical measure, such as the mean or median, calculated from the non-missing values of that variable. Other techniques include ignoring the missing entries or dropping the rows containing missing data, although this can potentially result in loss of information. The method chosen depends on the specifics of the dataset and the analysis being performed. . Response in Sesotho: Tsebetso ea data preprocessing ke mokhoa oa ho hloekisa le ho fetola data e tala hore e be sebopeho se utloisisoang habonolo le se loketseng ho hlahlojoa. Sepheo sa ho lokisa datha ke ho ntlafatsa boleng ba datha, ho e etsa hore e lokele ho sebelisoa ho ithuteng ka mochini, merafong ea data, le mefuta e meng ea tlhahlobo. Mohlala oa ho lokisa data esale pele ke ho sebetsana le data e sieo. Hangata, li-datasets li na le litekanyetso tse sieo kapa boitsebiso bo sa fellang bo ka nang ba kena-kenana le tlhahlobo ea ho qetela. Mekhoa ea ho lokisa data e ka sebelisoa ho khetholla, ho sebetsana le ho tlatsa lintlha tsena tse sieo tsa data ho etsa hore dataset e be e phethahetseng le e sebetsang. Mokhoa o mong oa ho sebetsana le data e sieo o bitsoa imputation - sena se kenyelletsa ho nkela data e sieo sebaka ka tekanyo ea lipalo-palo, joalo ka moelelo kapa bohareng, e baloang ho tsoa ho boleng bo sa sieo ba phapang eo. Mekhoa e meng e kenyelletsa ho hlokomoloha lintlha tse sieo kapa ho lahla mela e nang le data e sieo, leha sena se ka baka tahlehelo ea tlhahisoleseling. Mokhoa o khethiloeng o itšetlehile ka lintlha tse tobileng tsa dataset le tlhahlobo e etsoang. 