<prompt> Theha mohlala oa ho bolela esale pele ho bolela esale pele theko ea setoko ea khamphani X.nan  <response>: Instruction in English: Develop a prediction model to predict the stock price of company X. . Response in English: To develop a prediction model that predicts the stock price of company X, we would first need to gather data relevant to the company and its stock performance. This would include historical stock prices, financial reports, news articles or events that affect the stock market or the companys stocks specifically. Once this data is gathered, it can be preprocessed and cleaned to be ready for analysis.There are various techniquesused to forecast stock prices, such as linear regression, moving averages, decision trees or recurrent neural networks. We can select an appropriate model depending on the nature of our data and performance of each model.Once we have selected a model, we can train it on our data by dividing it into training and validation sets. The model is fitted on the training set and its predictions are compared with the actual stock prices from the validation set to calculate the error. This process can be repeated with different combinations of training and validation sets using techniques such as k-fold cross-validation to get an estimate of the models overall performance.After we are satisfied with the models accuracy, we can use it to predict future stock prices for Company X. The model can be updated as new data becomes available to keep the predictions accurate.However, it is important to note that stock price prediction is an inherently difficult task due to the unpredictability and volatility of the stock market, and no model can guarantee 100 accuracy. It is important to use the models predictions as guidance rather than absolute truth and also consider other factors that may affect stock price. . Response in Sesotho: Ho theha mofuta oa ho bolela esale pele o bolelang esale pele theko ea setoko ea khamphani X, re tla hloka pele ho bokella lintlha tse amanang le khamphani le tsebetso ea eona ea setoko. Sena se tla kenyelletsa litheko tsa nalane ea setoko, litlaleho tsa lichelete, lingoliloeng kapa liketsahalo tse amang maraka oa setoko kapa setoko sa khamphani ka ho khetheha. Hang ha lintlha tsena li bokelloa, li ka sebetsoa esale pele le ho hloekisoa hore li lokele ho hlahlojoa. Ho na le mekhoa e fapaneng e sebelisetsoang ho bolela esale pele litheko tsa setoko, joalo ka ho theoha ha mela, ho falla, lifate tsa liqeto kapa marang-rang a tloaelehileng a neural. Re ka khetha mohlala o nepahetseng ho latela mofuta oa data ea rona le tsebetso ea mofuta o mong le o mong. Ha re se re khethile mohlala, re ka o koetlisa ho data ea rona ka ho e arola ka lihlopha tsa koetliso le ho netefatsa. Mohlala o kentsoe setsing sa koetliso me likhakanyo tsa eona li bapisoa le theko ea sebele ea theko ho tloha ho tiiso e behiloeng ho bala phoso. Tsebetso ena e ka phetoa ka mefuta e fapaneng ea lithupelo le li-sete tsa netefatso ho sebelisoa mekhoa e kang k-fold cross-validation ho fumana khakanyo ea tsebetso e akaretsang ea mohlala. Ka mora hore re khotsofatsoe ke ho nepahala ha mohlala, re ka e sebelisa ho bolela esale pele litheko tsa theko ea nakong e tlang bakeng sa Khampani X. Mohlala o ka ntlafatsoa ha data e ncha e fumaneha ho boloka likhakanyo tse nepahetseng. Leha ho le joalo, ke habohlokoa ho hlokomela hore ho bolela esale pele theko ea theko ke mosebetsi o boima ka tlhaho ka lebaka la ho se tsejoe hantle le ho hloka botsitso ha maraka oa lichelete, me ha ho mohlala o ka tiisang ho nepahala ha 100. Ho bohlokoa ho sebelisa likhakanyo tsa mohlala e le tataiso ho fapana le nete e felletseng mme hape u nahane ka lintlha tse ling tse ka amang theko ea setoko. 