<prompt> Theha algorithm ho bolela esale pele mekhoa ea thekiso ea nakong e tlang.nan  <response>: Instruction in English: Create an algorithm to predict future sales trends. . Response in English: To predict future sales trends, we can use a combination of historical sales data and machine learning techniques to build a predictive model. Here are the steps for creating an algorithm to predict future sales trends:1. Gather and clean the data: Collect historical sales data and related information such as the date, product, price, and promotion. Clean the data to ensure there are no errors, missing values or outliers that may affect the accuracy of the model.2. Identify features: Determine which factors may influence sales trends, such as holidays, seasons, and marketing campaigns. Incorporate these as features in the dataset.3. Split the data: Divide the dataset into a training set for building the model, and a test set for evaluating its performance.4. Choose a model: Several machine learning algorithms can be used for sales forecasting, such as linear regression, decision trees or neural networks. Choose a model based on the size and nature of the data, and the desired level of accuracy.5. Train the model: Use the training dataset to train the model, adjusting its parameters to minimise the prediction error.6. Evaluate the model: Test the model on the test dataset to evaluate its performance. Use metrics such as mean squared error or mean absolute error to quantify the difference between the predicted and actual sales values.7. Make predictions: Once the model has been evaluated and refined, use it to make predictions on future sales trends. The model can also be updated periodically with new data to improve its accuracy over time. . Response in Sesotho: Ho bolela esale pele mekhoa ea thekiso ea nakong e tlang, re ka sebelisa motsoako oa data ea khale ea thekiso le mekhoa ea ho ithuta mochine ho haha mohlala oa ho bolela esale pele. Mehato ea ho theha algorithm ho bolela esale pele mekhoa ea thekiso ea nakong e tlang ke ena: 1. Bokella le ho hloekisa lintlha: Bokella lintlha tsa nalane ea thekiso le lintlha tse amanang le tsona joalo ka letsatsi, sehlahisoa, theko, le papatso. Hloekisa lintlha ho netefatsa hore ha ho na liphoso, litekanyetso tse sieo kapa lintho tse ka ntle tse ka amang ho nepahala ha mohlala. 2. Khetholla likarolo: Netefatsa hore na ke lintlha life tse ka susumetsang mekhoa ea thekiso, e kang matsatsi a phomolo, linako tsa selemo le matšolo a ho bapatsa. Kenyelletsa tsena e le likarolo ho pokello ea lintlha. 3. Arola lintlha: Arola dataset ho sete ea koetliso bakeng sa ho haha mohlala, le sete ea teko bakeng sa ho hlahloba tsebetso ea eona. 4. Khetha mohlala: Mekhoa e mengata ea ho ithuta mochine e ka sebelisoa bakeng sa ponelopele ea thekiso, joalo ka ho fokotseha ha mela, lifate tsa liqeto kapa marang-rang a methapo ea kutlo. Khetha mohlala ho latela boholo le mofuta oa data, le boemo bo lakatsehang ba ho nepahala. 5. Koetlisa mohlala: Sebelisa dataset ea koetliso ho koetlisa mohlala, ho lokisa maemo a eona ho fokotsa phoso ea ho bolela esale pele. 6. Lekola mohlala: Leka mohlala ho dataset ea teko ho hlahloba tsebetso ea eona. Sebelisa metrics joalo ka mean squared error kapa ho bolela phoso e felletseng ho fumana phapang lipakeng tsa litheko tse boletsoeng esale pele le tsa nete. 7. Etsa esale pele: Hang ha mohlala o se o hlahlobiloe le ho hloekisoa, o sebelise ho bolela esale pele ka mekhoa ea nakong e tlang ea thekiso. Mohlala o ka boela oa ntlafatsoa nako le nako ka lintlha tse ncha ho ntlafatsa ho nepahala ha nako. 