<prompt> Theha mokhoa oa ho ithuta mochini ho bolela esale pele theko ea setoko se fanoeng.Lintlha tsa nalane tsa litheko tsa letsatsi le letsatsi tsa li-stock tse tharo tsa khamphani ea theknoloji, e leng stock ea ABC, stock ea XYZ le stock ea PQR, ho tloha lilemong tse 5 tse fetileng.  <response>: Instruction in English: Develop a machine learning model to predict the price of a given stock. . Response in English: To develop a machine learning model to predict the price of a given stock, we can follow these steps:1. Data preprocessing: - First we need to clean and preprocess the data for the historical daily price of the three stocks, ABC, XYZ, and PQR. - We need to ensure that the data is complete, and there are no missing values or erroneous entries. - We will also need to split the data into training and test sets, with the majority of the data being used for training.2. Feature selection: - We will need to determine which features or variables are most relevant in predicting the stock prices. - These variables can include past prices, trading volumes, and other technical indicators. - Feature selection can be done through a variety of methods, including correlation analysis or using feature importance scores from a tree-based model.3. Model building: - We can experiment with different machine learning algorithms to determine which one is most effective in predicting stock prices. - Some common techniques used for stock price prediction include linear regression, decision trees, and neural networks. - We will also need to fine-tune the models hyperparameters to ensure that it can make accurate predictions.4. Model evaluation: - Once we have built and trained the model, we will need to test its performance against the test data. - We will use various performance metrics, such as mean squared error or root mean squared error, to assess the models accuracy. - If the model is not performing well, we may need to reconsider our feature selection or try different algorithms.5. Model deployment: - Once we are satisfied with the performance of our machine learning model, we can deploy it to make predictions on new data. - We can use the model to predict the stock prices of ABC, XYZ, and PQR, given the relevant input data. - As the model makes predictions, we can continually update it with new data to improve its accuracy over time.This is a high-level overview of how we could develop a machine learning model to predict stock prices. The specific details and implementation will depend on the data available and the requirements of the project. . Response in Sesotho: Ho hlahisa mohlala oa ho ithuta ka mochine ho bolela esale pele theko ea thepa e fanoeng, re ka latela mehato ena: 1. Tsebetso ea data esale pele: - Pele re hloka ho hloekisa le ho lokisa lintlha tsa histori tsa theko ea letsatsi le letsatsi ea li-stock tse tharo, ABC, XYZ, le PQR. - Re hloka ho netefatsa hore data e phethehile, me ha ho na litekanyetso tse sieo kapa lintlha tse fosahetseng. - Hape re tla hloka ho arola lintlha ka hara lithupelo le lihlopha tsa liteko, me boholo ba lintlha bo sebelisoa bakeng sa koetliso. 2. Khetho ea likarolo: - Re tla hloka ho tseba hore na ke likarolo life kapa mefuta efe e bohlokoa haholo ho lepa litheko tsa setoko. - Mefuta ena e ka kenyelletsa litheko tse fetileng, meqolo ea khoebo, le matšoao a mang a tekheniki. - Khetho ea likarolo e ka etsoa ka mekhoa e fapaneng, ho kenyelletsa tlhahlobo ea khokahano kapa ho sebelisa lintlha tsa bohlokoa ho tsoa moetsong o thehiloeng sefateng. 3. Moaho oa mohlala: - Re ka etsa liteko ka li-algorithms tse fapaneng tsa ho ithuta ka mochini ho fumana hore na ke ofe o sebetsang haholo ho bolela esale pele litheko tsa setoko. - Mekhoa e meng e tloaelehileng e sebelisoang bakeng sa ho bolela esale pele theko ea setoko e kenyelletsa ho theoha ha mela, lifate tsa liqeto, le marang-rang a methapo. - Hape re tla hloka ho hlophisa li-hyperparameter tsa mohlala ho netefatsa hore e ka etsa likhakanyo tse nepahetseng. 4. Tlhahlobo ea mohlala: - Ha re se re hahile le ho koetlisa mohlala, re tla hloka ho hlahloba tsebetso ea oona khahlanong le data ea teko. - Re tla sebelisa metrics e fapaneng ea tsebetso, joalo ka phoso e bolelang squared error kapa root mean squared error ho hlahloba ho nepahala ha mohlala. - Haeba mohlala o sa sebetse hantle, ho ka na ha hlokahala hore re hlahlobisise khetho ea rona ea likarolo kapa re leke li-algorithms tse fapaneng. 5. Tšebeliso ea mohlala: - Hang ha re khotsofetse ke tsebetso ea mohlala oa rona oa ho ithuta mochine, re ka o sebelisa ho etsa likhakanyo ho data e ncha. - Re ka sebelisa mohlala ho bolela esale pele litheko tsa li-stock tsa ABC, XYZ, le PQR, ha re fuoa lintlha tse amehang tse kentsoeng. - Ha mohlala o etsa likhakanyo, re ka lula re o nchafatsa ka lintlha tse ncha ho ntlafatsa ho nepahala ha nako. Ena ke kakaretso ea boemo bo holimo ea hore na re ka theha mohlala oa ho ithuta oa mochini joang ho bolela esale pele litheko tsa setoko. Lintlha tse tobileng le tsebetsong li tla itšetleha ka lintlha tse teng le litlhoko tsa morero. 