<prompt> Ho sebetsa pele ho data ho sebelisoa joang thutong ea mochini?nan  <response>: Instruction in English: How is data pre-processing used in machine learning? . Response in English: Data preprocessing is a crucial step in machine learning that involves transforming and preparing raw data into an appropriate and suitable format for use in training machine learning models. There are several techniques used in data pre-processing, including:1. Data cleansing: involves removing any incorrect, incomplete or inconsistent data, and filling in missing values through means such as interpolation or using statistical methods. This ensures the data used for training is accurate and consistent.2. Data normalization: involves scaling the data to ensure all feature values fall within the same range, typically between 0 and 1. This helps to prevent any feature from dominating the others, as larger values could overshadow smaller values, making it difficult for the algorithm to learn effectively. 3. Data Transformation: involves converting data into a format that can be easily analyzed and understood by the machine learning algorithms. This involves tasks such as encoding categorical data or reducing the dimensionality of the data. 4. Data integration: involves combining data from different sources to create a unified dataset. This is important as it ensures that all relevant data that may affect the models performance is included in the training data.5. Feature selection: involves selecting the most important features to include in the model. This can prevent overfitting, and can help to reduce the complexity of the model. Overall, data preprocessing is essential for improving the quality of data, make it structured and consistent, and enhancing the accuracy and performance of machine learning models. . Response in Sesotho: Tsebetso ea data esale pele ke mohato oa bohlokoa thutong ea mochini o kenyelletsang ho fetola le ho lokisa data e tala hore e be sebopeho se nepahetseng le se loketseng ho sebelisoa mefuteng ea ho ithuta ea mochini. Ho na le mekhoa e maloa e sebelisoang ha ho etsoa data pele ho tsebetso, ho kenyelletsa: 1. Ho hloekisa lintlha: ho kenyelletsa ho tlosa lintlha leha e le life tse fosahetseng, tse sa phethahalang kapa tse sa tsitsang, le ho tlatsa litekanyetso tse sieo ka mekhoa e kang ho kenyelletsa kapa ho sebelisa mekhoa ea lipalo-palo. Sena se tiisa hore data e sebelisoang bakeng sa koetliso e nepahetse ebile e tsitsitse. 2. Ho tloaeleha ha data: ho kenyelletsa ho lekola lintlha ho netefatsa hore boleng bohle bo oela ka hara mefuta e tsoanang, hangata lipakeng tsa 0 le 1. Sena se thusa ho thibela tšobotsi efe kapa efe ho laola tse ling, kaha litheko tse kholo li ka pupetsa litheolelo tse nyane, tsa etsa hore ho be thata algorithm ho ithuta ka katleho. 3. Phetoho ea Boitsebiso: e kenyelletsa ho fetola boitsebiso ka mokhoa o ka hlahlojoang habonolo le ho utloisisoa ke li-algorithms tsa ho ithuta mochine. Sena se kenyelletsa mesebetsi e joalo ka khouto data ea likarolo kapa ho fokotsa boholo ba data. 4. Ho kopanya lintlha: ho kenyelletsa ho kopanya lintlha tse tsoang mehloling e fapaneng ho theha dataset e kopaneng. Sena ke sa bohlokoa kaha se tiisa hore lintlha tsohle tse amehang tse ka amang tsebetso ea mohlala li kenyelelitsoe ho data ea koetliso. 5. Khetho ea likarolo: e kenyelletsa ho khetha likarolo tsa bohlokoa ka ho fetisisa ho kenyelletsa mohlala. Sena se ka thibela ho fetella, me se ka thusa ho fokotsa ho rarahana ha mohlala. Ka kakaretso, tsebetso ea data esale pele e bohlokoa bakeng sa ho ntlafatsa boleng ba data, ho e etsa hore e hlophisehe, e tsoane, le ho ntlafatsa ho nepahala le tsebetso ea mefuta ea ho ithuta ka mochini. 