<prompt> Theha mohlala oa linear regression ho bolela esale pele thekiso.Lethathamo le na le lintlha tse mabapi le litheko tsa lihlahisoa le palo ea batho ba bareki.  <response>: Instruction in English: Construct a linear regression model to predict sales. . Response in English: Okay, I can help you with that. Linear regression is a supervised machine learning approach that tries to predict a continuous target variable based on one or more independent variables by fitting a line of best fit to the data. In our case, wed be predicting sales based on product prices and customer demographics.First, we would need to clean and preprocess the data. This involves removing any missing or incorrect values, handling outliers or transforming the data if necessary. And also we need to encode any categorical variables, such as demographics, so that the model can interpret them numerically.Once the data is preprocessed, well split it into training and testing datasets. The training data will be used to build the model, whereas the testing data will be used to evaluate its performance.Now, we can train the model by providing it the training data and specifying the target variable - sales - and the independent variables - product prices and customer demographics.Then, well fit a linear regression model to the data by finding the line of best fit that minimizes the residual sum of squares.Finally, we can use the testing data to make predictions and evaluate the models performance. Common evaluation metrics for regression problems include the mean absolute error MAE, mean squared error MSE, and root mean squared error RMSE. Well want to select the model that performs the best on the testing data.Thats a general overview of how wed approach constructing a linear regression model to predict sales based on product prices and customer demographics. Is there anything else you need help with? . Response in Sesotho: Ho lokile, nka o thusa ka seo. Linear regression ke mokhoa o hlokometsoeng oa ho ithuta mochini o lekang ho lepa phetoho e tsoelang pele ea sepheo ho ipapisitsoe le mofuta o le mong kapa tse ngata tse ikemetseng ka ho kenya mohala o loketseng data. Tabeng ea rona, re tla be re bolela esale pele thekiso ho latela litheko tsa sehlahisoa le palo ea batho ba bareki. Taba ea pele, re tla hloka ho hloekisa le ho lokisa data esale pele. Sena se kenyelletsa ho tlosa litekanyetso tse sieo kapa tse fosahetseng, ho sebetsana le li-outliers kapa ho fetola data ha ho hlokahala. Hape re hloka ho kenyelletsa mefuta efe kapa efe ea likarolo, joalo ka palo ea batho, e le hore mohlala o ka li hlalosa ka lipalo. Hang ha data e se e sebelitsoe esale pele, re tla e arola ka li-database tsa koetliso le liteko. Lintlha tsa koetliso li tla sebelisoa ho aha mohlala, athe data ea tlhahlobo e tla sebelisoa ho lekola tsebetso ea eona. Joale, re ka koetlisa mohlala ka ho o fa lintlha tsa koetliso le ho hlakisa sepheo sa sepheo - thekiso - le mefuta e ikemetseng - litheko tsa lihlahisoa le palo ea batho ba bareki. Ka mora moo, re tla lumellana le mohlala oa tatellano ea morao-rao ho data ka ho fumana moeli o lekanang hantle o fokotsang palo e setseng ea lisekoere. Qetellong, re ka sebelisa lintlha tsa tlhahlobo ho etsa likhakanyo le ho lekola tsebetso ea mohlala. Metrics e tloaelehileng ea tlhahlobo bakeng sa mathata a ho khutlela morao e kenyelletsa phoso e tloaelehileng MAE, mean squared error MSE, le root mean squared error RMSE. Re tla batla ho khetha mofuta o sebetsang hantle ho data ea tlhahlobo. Eo ke kakaretso e akaretsang ea hore na re ka etsa joang ho theha mofuta oa ho theola maemo ho bolela esale pele thekiso e ipapisitseng le litheko tsa sehlahisoa le palo ea batho ba bareki. Na ho na le ntho e ngoe eo u hlokang thuso ka eona? 