<prompt> Hlahisa lipeipi tsa boenjiniere ba likarolo.nan  <response>: Instruction in English: Generate a feature engineering pipeline. . Response in English: A feature engineering pipeline is a series of steps used to extract, transform, and select the most relevant features from a dataset in order to build a predictive model. Here are the steps that can be used to create a feature engineering pipeline:1. Data collection and cleaning: The first step is to gather and clean the data. This can be done by removing any duplicate or irrelevant observations, handling missing or incomplete data, and converting categorical data into numerical data.2. Exploratory Data Analysis: The next step is to perform exploratory data analysis EDA to get a better understanding of the data. This includes visualizing the data and calculating various statistical measures, such as correlation, mean, and standard deviation.3. Feature extraction: Feature extraction is the process of deriving new features from the existing ones, such as taking the square, logarithm or square root of a variable or creating interaction terms between two or more variables.4. Feature transformation: The next step is to transform the features to make them more suitable for modeling. This can include scaling features to have zero mean and unit variance, normalizing features to the same range, or applying other transformation methods, such as box-cox or Yeo-Johnson.5. Feature selection: Feature selection is the process of choosing a subset of the features that are most relevant to the target variable, by using various techniques such as LASSO, backward elimination, forward selection or principal component analysis PCA.6. Modeling: The final step is to use the selected features to build and train a predictive model, such as a decision tree, random forest, or logistic regression.The above steps can be implemented using various programming languages and tools such as Python, R, scikit-learn, and pandas, and can be customized and refined based on the specific requirements of the data and the model. . Response in Sesotho: Phaephe ea boenjineri ke letoto la mehato e sebelisoang ho hula, ho fetola, le ho khetha likarolo tse amehang ho tsoa pokellong ea data e le ho aha mohlala oa ho lepa. Mehato e ka sebelisoang ho theha phaephe ea boenjiniere ea likarolo: 1. Pokello ea data le ho hloekisa: Mohato oa pele ke ho bokella le ho hloekisa lintlha. Sena se ka etsoa ka ho tlosa lintlha tse qotsitsoeng kapa tse sa hlokahaleng, ho sebetsana le lintlha tse sieo kapa tse sa fellang, le ho fetolela data ea likarolo hore e be data ea linomoro. 2. Tlhahlobo ea Boitsebiso: Mohato o latelang ke ho etsa tlhahlobo ea data ea lipatlisiso EDA ho fumana kutloisiso e molemo ea lintlha. Sena se kenyelletsa ho bona lintlha ka mahlo le ho bala mehato e fapaneng ea lipalo, joalo ka khokahano, moelelo, le ho kheloha ho tloaelehileng. 3. Ntsollo ea tšobotsi: Ho qotsa tšobotsi ke mokhoa oa ho fumana likarolo tse ncha ho tse seng li ntse li le teng, joalo ka ho nka sekoere, logarithm kapa lisekoere motso oa ntho e feto-fetohang kapa ho theha mareo a puisano pakeng tsa mefuta e meli kapa ho feta. 4. Phetoho ea likarolo: Mohato o latelang ke ho fetola likarolo ho li etsa hore li tšoanelehe haholoanyane bakeng sa mohlala. Sena se ka kenyelletsa likarolo tsa ho lekanya ho ba le zero moelelo le phapang ea yuniti, likarolo tse tloaelehileng ho isa boemong bo tšoanang, kapa ho sebelisa mekhoa e meng ea phetoho, joalo ka box-cox kapa Yeo-Johnson. 5. Khetho ea likarolo: Khetho ea likarolo ke mokhoa oa ho khetha karoloana ea likarolo tse amehang haholo ho feto-fetoha ha sepheo, ka ho sebelisa mekhoa e fapaneng e kang LASSO, ho felisa morao, ho khetha pele kapa tlhahlobo ea karolo e ka sehloohong PCA . 6. Ho etsa mohlala: Bohato ba ho qetela ke ho sebelisa likarolo tse khethiloeng ho aha le ho koetlisa mohlala oa ho lepa, joalo ka sefate sa liqeto, moru o sa reroang, kapa ho theoha ha thepa. Mehato e ka holimo e ka kenngoa tsebetsong ka lipuo tse fapaneng tsa lenaneo le lisebelisoa tse kang Python, R, scikit-learn, le pandas, me li ka etsoa le ho ntlafatsoa ho latela litlhoko tse khethehileng tsa data le mohlala. 