<prompt> Mehato ea mantlha ea ho hloekisa data ke efe?nan  <response>: Instruction in English: What are the main steps in the process of data cleaning? . Response in English: Data cleaning is the process of identifying and modifying or removing inaccurate, incomplete, or irrelevant data from a dataset. The main steps in the process of data cleaning are:1. Data Audit: The first step in data cleaning is to audit the data by running statistical summaries and checking for any irregularities or inconsistencies. This will help you identify any errors and outliers in the dataset.2. Data Validation: The second step is to validate the data to ensure that it is accurate and conforms to specified rules or business requirements. This can be done manually or by using automated tools.3. Handling Missing Data: The third step involves dealing with missing data. You can either remove records with missing data, fill in the missing data with default values or statistical methods, or ignore it entirely depending on the nature and amount of missing data.4. Handling Duplicate Data: The next step is to identify and remove any duplicate data. Duplicates can occur due to data entry errors or merging datasets from multiple sources.5. Outlier detection and treatment: Identify and treat outliers or anomalies that can skew the analysis. Outliers can be removed, corrected, or retained based on domain knowledge or by using statistical methods like the Interquartile Range or Z-score method.6. Data Transformation: After cleaning the data, the next step is to transform it into an appropriate format that can be used for further analysis. This involves normalizing, scaling, or encoding data.7. Data Verification: The final step in the data cleaning process is to verify that the data has been cleaned accurately. This can be done by performing data audits or conducting quality checks on the cleaned data.Effective data cleaning is essential to ensure the accurate and reliable analysis of data. It is an iterative process that involves checking, validating, and updating data to maintain its accuracy and completeness. . Response in Sesotho: Ho hloekisa data ke mokhoa oa ho khetholla le ho fetola kapa ho tlosa lintlha tse sa nepahalang, tse sa fellang, kapa tse sa hlokahaleng ho tsoa ho dataset. Mehato e ka sehloohong tsebetsong ea ho hloekisa data ke: 1. Tlhahlobo ea Boitsebiso: Mohato oa pele oa ho hloekisa data ke ho hlahloba lintlha ka ho etsa likakaretso tsa lipalo-palo le ho hlahloba hore na ha ho na mekhoa e sa tšoaneng kapa e sa lumellaneng. Sena se tla u thusa ho tseba liphoso le li-outliers ho dataset. 2. Tlhahlobo ea Boitsebiso: Mohato oa bobeli ke ho netefatsa lintlha ho netefatsa hore li nepahetse ebile li lumellana le melao e boletsoeng kapa litlhoko tsa khoebo. Sena se ka etsoa ka letsoho kapa ka ho sebelisa lisebelisoa tse ikemetseng. 3. Ho sebetsana le Lintlha tse Sieo: Mohato oa boraro o kenyelletsa ho sebetsana le data e sieo. U ka tlosa lirekoto ka data e sieo, tlatsa lintlha tse sieo ka litekanyetso tsa kamehla kapa mekhoa ea lipalo-palo, kapa u iphapanyetsa eona ka botlalo ho latela mofuta le palo ea data e sieo. 4. Ho sebetsana le Boitsebiso bo Bobeli: Mohato o latelang ke ho tseba le ho tlosa data efe kapa efe e kopitsoang. Likopi li ka hlaha ka lebaka la liphoso tsa ho kenya data kapa ho kopanya li-dataset ho tsoa mehloling e mengata. 5. Ho lemoha le ho alafa lintho tse kantle: Tseba le ho alafa lintho tse ka ntle kapa tse sa hlakang tse ka khopamisang tlhahlobo. Li-outliers li ka tlosoa, tsa lokisoa, kapa tsa bolokoa ho ipapisitsoe le tsebo ea domain kapa ka ho sebelisa mekhoa ea lipalo-palo joalo ka Interquartile Range kapa mokhoa oa Z-score. 6. Phetoho ea Boitsebiso: Ka mora ho hloekisa lintlha, mohato o latelang ke ho li fetola ka mokhoa o nepahetseng o ka sebelisoang bakeng sa tlhahlobo e tsoelang pele. Sena se kenyelletsa ho tloaeleha, ho lekanya, kapa ho kenya data. 7. Netefatso ea Lintlha: Mohato oa ho qetela oa ho hloekisa data ke ho netefatsa hore data e hloekisitsoe ka nepo. Sena se ka etsoa ka ho etsa litlhahlobo tsa data kapa ho hlahloba boleng ba data e hloekisitsoeng. Ho hloekisa data ka katleho ho bohlokoa ho netefatsa tlhahlobo e nepahetseng le e tšepahalang ea data. Ke tsebetso e pheta-phetoang e kenyelletsang ho lekola, ho netefatsa, le ho nchafatsa data ho boloka ho nepahala le botlalo ba eona. 