<prompt> Rala mokhoa oa ho sebetsa bakeng sa sistimi ea ho ithuta ea mochini ho bolela esale pele litheko tsa setoko.nan  <response>: Instruction in English: Design a workflow for a machine learning system to predict stock prices. . Response in English: 1. Data Collection: The first step in designing a workflow for predicting stock prices is to collect relevant data. This may include historical stock prices, market trends, and economic indicators. Additionally, the data can be enriched by including data from external sources such as company earnings reports, news articles, and social media sentiment analysis.2. Data Preprocessing: Once the data is collected, it needs to be preprocessed to make it suitable for analysis. This includes steps such as cleaning the data, handling missing values and outliers, and normalizing the data to reduce the impact of extreme values.3. Feature Engineering: Next, the data needs to be transformed into relevant features that can be used by the machine learning algorithm. This can involve techniques such as dimensionality reduction, feature scaling, and feature selection.4. Model Selection: In this step, the most suitable machine learning model for the task is selected. This can involve evaluating various algorithms and choosing the one that has the best performance on the training data. Models such as Random Forest, Regression Analysis, and Neural Networks are often used for stock price prediction.5. Model Training: Once the model is selected, it is trained on the pre-processed data. This involves adjusting the parameters of the model to minimize the error between the predicted and the actual stock prices.6. Model Evaluation: The next step is to evaluate the model on a separate set of data to assess its performance. This can involve calculating measures such as the mean squared error and the coefficient of determination R2 to determine the accuracy of the model.7. Prediction: Once the model has been trained and evaluated, it can be used to make predictions on future stock prices.8. Monitoring and Updating: The final step in the workflow is to continuously monitor the performance of the model and update it periodically to account for any changes in the market. This involves re-training the model on new data and adjusting its parameters as needed to maintain its accuracy. . Response in Sesotho: 1. Pokello ea Boitsebiso: Mohato oa pele oa ho theha mokhoa oa ho sebetsa bakeng sa ho bolela esale pele litheko tsa thepa ke ho bokella lintlha tse nepahetseng. Sena se ka kenyelletsa litheko tsa nalane ea setoko, mekhoa ea maraka, le matšoao a moruo. Ho feta moo, data e ka ntlafatsoa ka ho kenyelletsa lintlha tse tsoang mehloling e kantle joalo ka litlaleho tsa meputso ea khamphani, lingoliloeng tsa litaba le tlhahlobo ea maikutlo a litaba tsa sechaba. 2. Phatlalatso ea Boitsebiso: Hang ha boitsebiso bo bokelloa, bo hloka ho lokisoa esale pele ho etsa hore bo lokele ho hlahlojoa. Sena se kenyelletsa mehato e kang ho hloekisa lintlha, ho sebetsana le litekanyetso tse sieo le li-outliers, le ho tloaeleha ha data ho fokotsa tšusumetso ea litekanyetso tse feteletseng. 3. Boenjineri ba Tšobotsi: Ka mora moo, lintlha li hloka ho fetoloa likarolo tse amehang tse ka sebelisoang ke algorithm ea ho ithuta mochine. Sena se ka kenyelletsa mekhoa e kang ho fokotsa boholo, ho phahamisa likarolo, le khetho ea likarolo. 4. Khetho ea Mohlala: Mohato ona, mokhoa o loketseng ka ho fetisisa oa ho ithuta mochine bakeng sa mosebetsi o khethiloe. Sena se ka kenyelletsa ho lekola li-algorithms tse fapaneng le ho khetha e nang le tsebetso e ntle ka ho fetisisa ho data ea koetliso. Mehlala e kang Random Forest, Regression Analysis, le Neural Networks e atisa ho sebelisoa bakeng sa ho bolela esale pele theko ea thepa. 5. Koetliso ea Mohlala: Hang ha mohlala o khethiloe, o koetlisetsoa ho data e entsoeng esale pele. Sena se kenyelletsa ho lokisa liparamente tsa mohlala ho fokotsa phoso lipakeng tsa litheko tse boletsoeng esale pele le litheko tsa nete. 6. Tlhahlobo ea Mohlala: Mohato o latelang ke ho hlahloba mohlala ka sete e fapaneng ea data ho hlahloba tshebetso ea eona. Sena se ka kenyelletsa ho bala mehato e joalo ka mean squared error le coefficient of determination R2 ho fumana bonnete ba mohlala. 7. Ho bolela esale pele: Hang ha mohlala o se o koetlisitsoe le ho hlahlojoa, o ka sebelisoa ho bolela esale pele ka litheko tsa nakong e tlang. 8. Tlhokomelo le Nchafatso: Mohato oa ho qetela mosebetsing ke ho tsoela pele ho shebella tsebetso ea mohlala le ho e ntlafatsa nako le nako ho ikarabella bakeng sa liphetoho leha e le life marakeng. Sena se kenyelletsa ho koetlisa mohlala hape mabapi le data e ncha le ho lokisa liparamente tsa eona ha ho hlokahala ho boloka bonnete ba eona. 