<prompt> Theha tataiso ea mehato e 10 bakeng sa ho lokisa lintlha tsa projeke ea ho ithuta ka mochini.nan  <response>: Instruction in English: Create a 10-step guide for data pre-processing for a machine learning project. . Response in English: 1. Define the problem and objectives: Identify the specific problem or question that the machine learning project is trying to solve, and determine how the data will be used to address the problem.2. Obtain the data: Collect the raw data needed for the project from relevant sources. Carefully choose the data based on its reliability and suitability for the problem.3. Data Exploration: Analyze the data to understand its characteristics and patterns through techniques such as data visualization and descriptive statistics. Identify any inconsistencies, outliers, and missing values.4. Data Cleaning: Correct any errors or inconsistencies in the data by removing duplicates or irrelevant observations, and resolving any inaccuracies or conflicts.5. Handling missing data: Missing data can be handled in different ways such as deleting the entire observation, imputing the missing values using different techniques or replacing the missing value with a constant value. The approach should be chosen based on the nature of the data and the problem.6. Data Transformation: Transform the data into a format appropriate for analysis by the machine learning algorithm. This may involve techniques such as scaling, normalization, encoding, or variable creation.7. Data Reduction: Reduce the dimensionality of the data by selecting a subset of relevant features, or through techniques such as Principal Component Analysis PCA or Linear Discriminant Analysis LDA.8. Data Integration: Combine data from multiple sources to create a complete dataset. This may involve merging or joining datasets, or concatenating data from multiple sources.9. Splitting the data: Split the data into training and test sets. This allows for the evaluation of the models performance on unseen data.10. Data verification: Verify that the data is in proper format and ready for analysis by the machine learning algorithm. Check for any remaining inconsistencies or errors that may have been overlooked in the earlier steps. . Response in Sesotho: 1. Hlalosa bothata le merero : Hlalosa bothata bo itseng kapa potso eo morero oa ho ithuta mochine o lekang ho e rarolla, me u fumane hore na data e tla sebelisoa joang ho rarolla bothata. 2. Fumana lintlha: Bokella lintlha tse hlokahalang bakeng sa morero ho tsoa mehloling e amehang. Khetha ka hloko lintlha tse thehiloeng ho tseptjoang le ho tšoaneleha ha bothata. 3. Tlhahlobo ea Lintlha: Sekaseka lintlha ho utloisisa litšobotsi le mekhoa ea eona ka mekhoa e kang pono ea data le lipalo-palo tse hlalosang. Lemoha ho se lumellane hofe kapa hofe, li-outliers, le litekanyetso tse sieo. 4. Tlhoekiso ea Lintlha: Lokisa liphoso leha e le life kapa ho se lumellane ho data ka ho tlosa tse kopitsoang kapa litebello tse sa lokelang, le ho rarolla ho se nepahale kapa likhohlano. 5. Ho sebetsana le data e sieo : Lintlha tse sieo li ka sebetsanoa ka litsela tse fapaneng tse kang ho hlakola pono eohle, ho beha litekanyetso tse sieo ho sebelisa mekhoa e fapaneng kapa ho nkela boleng bo sieo sebaka ka boleng bo sa feleng. Mokhoa o lokela ho khethoa ho latela mofuta oa data le bothata. 6. Phetoho ea Boitsebiso: Fetolela boitsebiso ka mokhoa o loketseng bakeng sa ho hlahlojoa ke algorithm ea ho ithuta mochine. Sena se ka kenyelletsa mekhoa e kang ho lekanya, ho tloaeleha, khouto, kapa tlhahiso e fapaneng. 7. Phokotso ea Boitsebiso: Fokotsa boholo ba lintlha ka ho khetha sehlotšoana sa likarolo tse amehang, kapa ka mekhoa e kang Principal Component Analysis PCA kapa Linear Discriminant Analysis LDA. 8. Khokahano ea Lintlha: Kopanya data ho tsoa mehloling e mengata ho theha pokello ea data e felletseng. Sena se ka kenyelletsa ho kopanya kapa ho kopanya datha, kapa ho kopanya data ho tsoa mehloling e mengata. 9. Ho arola lintlha: Arola lintlha ka lithupelo le lihlopha tsa liteko. Sena se lumella ho lekola tsebetso ea mohlala ho data e sa bonahaleng. 10. Netefatso ea data: Netefatsa hore data e ka sebopeho se nepahetseng me e loketse ho hlahlojoa ho latela algorithm ea ho ithuta ka mochini. Hlahloba ho se lumellane ho hong ho setseng kapa liphoso tseo e kanna eaba li ile tsa hlokomolohuoa mehatong ea pejana. 