<prompt> Ke mofuta oa mofuta ofe oa ho arola o ka loketseng mosebetsi o latelang?Ho bolela esale pele theko ea thepa ka mora beke  <response>: Instruction in English: Which type of classifaction model would be suitable for the following task? . Response in English: Predicting the price of a stock after one week is not a classification task, rather it is a regression task. Classification involves predicting a categorical label, while regression involves predicting a continuous numerical value.For stock price prediction, a suitable model for this regression task could be Time Series forecasting models such as ARIMA, SARIMA, and LSTM, which take into account the temporal nature of the data. Other machine learning models such as Random Forest, Support Vector Regression SVR, and Linear Regression could also be used. Furthermore, its also important to note that stock price prediction is a very challenging task, and these models might not always provide accurate predictions. The performance of these models highly depends on the quality of data, the selection of features, and how well the parameters have been tuned. . Response in Sesotho: Ho bolela esale pele theko ea thepa ka mora beke e le ngoe hase mosebetsi oa ho arola, ho e-na le hoo ke mosebetsi oa ho khutlela morao. Ho arola ka lihlopha ho kenyelletsa ho bolela esale pele leibole ea likarolo, ha ho fokotseha ho kenyelletsa ho bolela boleng bo tsoelang pele ba linomoro. Bakeng sa ho bolela esale pele ka theko ea thepa, mohlala o loketseng bakeng sa mosebetsi ona oa ho fokotseha e ka ba mehlala ea ho bolela esale pele ea Time Series e kang ARIMA, SARIMA, le LSTM, e nahanang ka boemo ba nakoana ba data. Mefuta e meng ea ho ithuta ka mochini e kang Random Forest, Support Vector Regression SVR, le Linear Regression le eona e ka sebelisoa. Ho feta moo, ho bohlokoa hape ho hlokomela hore ho bolela esale pele litheko tsa setoko ke mosebetsi o boima haholo, me mefuta ena e kanna ea se fane ka likhakanyo tse nepahetseng kamehla. Tsebetso ea mefuta ena e itšetlehile haholo ka boleng ba data, khetho ea likarolo, le hore na li-parameter li hlophisitsoe hantle hakae. 