<prompt> Hlalosa mehato ea ho lokisa data esale pele morerong oa ho ithuta ka mochininan  <response>: Instruction in English: Describe the data pre-processing steps in a machine learning project . Response in English: Data preprocessing is a crucial step in any machine learning project. It involves cleaning, transforming and restructuring the data to make it ready for use in building predictive models. The following are the main data preprocessing steps in a machine learning project. 1. Data Collection: The first step is to collect data from various sources such as databases, files, or web APIs. The data needs to be gathered in a structured format for easier processing.2. Data Cleaning: This is the process of getting rid of incorrect, incomplete, improperly formatted, or duplicated data. Missing values can be handled by either deleting or filling them using various data imputation techniques, while outliers and incorrect data can be removed or corrected.3. Data Transformation: This involves converting the data into a suitable format for use in machine learning algorithms. This includes scaling or normalizing the data, encoding categorical data, or transforming skewed data using techniques such as log transformation.4. Data Integration: If the data is collected from multiple sources, it must be combined and integrated into a consistent format.5. Data Reduction: Large datasets can be expensive to store, process and analyze, and may cause issues such as over-fitting in machine learning models. Dimensionality reduction techniques such as Principal Component Analysis PCA can be used to reduce the number of features while retaining their important information.6. Data Splitting: Finally, the data is split into training, validation, and testing sets. The training set is used to train the machine learning model, the validation set is used to fine-tune the model by optimizing its hyperparameters, and the testing set is used to evaluate its performance.In summary, data preprocessing is an important step in machine learning that should be carefully performed to ensure the data is of high quality and suitable for building robust and accurate predictive models. . Response in Sesotho: Ho lokisa data esale pele ke mohato oa bohlokoa morerong ofe kapa ofe oa ho ithuta oa mochini. E kenyelletsa ho hloekisa, ho fetola le ho hlophisa bocha data ho e etsa hore e lokele ho sebelisoa ho aha mefuta e boletsoeng esale pele. Lintlha tse latelang ke mehato ea mantlha ea ho lokisa data pele ho projeke ea ho ithuta ka mochini. 1. Pokello ea Lintlha: Mohato oa pele ke ho bokella lintlha ho tsoa mehloling e fapaneng e kang databases, lifaele, kapa web APIs. Lintlha li lokela ho bokelloa ka mokhoa o hlophisitsoeng bakeng sa ho sebetsa habonolo. 2. Tlhoekiso ea Boitsebiso: Ena ke mokhoa oa ho tlosa lintlha tse fosahetseng, tse sa fellang, tse hlophisitsoeng ka tsela e fosahetseng, kapa tse kopitsoang. Litekanyetso tse sieo li ka sebetsanoa ka ho li hlakola kapa ho li tlatsa ka mekhoa e fapaneng ea ho kenyelletsa data, athe li-outliers le lintlha tse fosahetseng li ka tlosoa kapa tsa lokisoa. 3. Phetoho ea Boitsebiso: Sena se kenyelletsa ho fetola boitsebiso ka mokhoa o loketseng bakeng sa ho sebelisoa ka mokhoa oa ho ithuta ka mochine. Sena se kenyeletsa ho lelefatsa kapa ho etsa hore datha e tloaelehe, ho khouta lintlha tsa mofuta o itseng, kapa ho fetola lintlha tse khopameng ho sebelisa mekhoa e joalo ka phetoho ea log. 4. Tšebeliso ea Boitsebiso: Haeba boitsebiso bo bokelloa ho tsoa mehloling e mengata, e tlameha ho kopanngoa le ho kenngoa ka mokhoa o tsitsitseng. 5. Phokotso ea Boitsebiso: Li-database tse kholo li ka bitsa chelete e ngata ho li boloka, ho li sebetsa le ho li hlahloba, me li ka baka mathata a kang ho lekana ho feta mefuta ea ho ithuta ka mochine. Mekhoa ea ho fokotsa likarolo tse kang Principal Component Analysis PCA e ka sebelisoa ho fokotsa palo ea likarolo ha u ntse u boloka boitsebiso ba bona ba bohlokoa. 6. Karohano ea Lintlha: Qetellong, data e arotsoe ka lithupelo, ho netefatsa, le lihlopha tsa liteko. Sehlopha sa koetliso se sebelisetsoa ho koetlisa mohlala oa ho ithuta mochine, sete sa ho netefatsa se sebelisetsoa ho lokisa mohlala ka ho ntlafatsa li-hyperparameter tsa oona, me sete sa tlhahlobo se sebelisetsoa ho lekola tshebetso ea sona. Ka kakaretso, tsebetso ea data preprocessing ke mohato oa bohlokoa oa ho ithuta mochine o lokelang ho etsoa ka hloko ho netefatsa hore data ke ea boleng bo phahameng me e loketse ho haha mefuta e matla le e nepahetseng ea ho bolela esale pele. 