<prompt> Sebelisa mekhoa ea ho ithuta ka mochini ho phethela mosebetsi onaHlalosa hore na ke lihlahisoa life tse tla ba le kholo e kholo ea thekiso kotareng e tlang  <response>: Instruction in English: Use machine learning methods to complete this task . Response in English: To predict which products will have the largest sales growth in the next quarter, we can use machine learning methods. Here is a step by step process on how this can be done:1. Gather Data: The first step is to gather historical sales data for all products for at least the past year. This data can be used to train the machine learning model. The data should include features such as product name, sales, date of sales, and any other related factors that can affect sales such as promotions, holidays or seasonal trends.2. Preprocessing: Clean the data to remove any missing or incorrect values. Data preprocessing also involves transforming the data into a format that can be used by the machine learning algorithms.3. Feature Selection: Select the most relevant features that have a strong correlation with the sales growth of the products.4. Model Selection: Choose a suitable machine learning algorithm for this task. Some algorithms that can be used for sales prediction include Linear Regression, Decision Trees, and Random Forest. 5. Training: Train the machine learning algorithm using the historical sales data. This involves feeding the algorithm with both the input features and the target variable which is the sales growth in this case.6. Evaluation: Evaluate the performance of the trained model using a suitable evaluation metric. This can be done by dividing the dataset into a training and testing set, training the algorithm on the training set and evaluating its performance on the testing set.7. Prediction: Once the model has been trained and evaluated, it can be used to make predictions on which products are likely to have the largest sales growth in the next quarter. It is important to note that machine learning models may need to be retrained periodically as new data becomes available to maintain their accuracy at predicting sales growth. . Response in Sesotho: Ho bolela esale pele hore na ke lihlahisoa life tse tla ba le kholo e kholo ea thekiso kotareng e tlang, re ka sebelisa mekhoa ea ho ithuta ka mochini. Mona ke mohato ka mohato mabapi le hore na sena se ka etsoa joang: 1. Bokella Lintlha: Mohato oa pele ke ho bokella lintlha tsa histori tsa thekiso bakeng sa lihlahisoa tsohle bonyane selemong se fetileng. Lintlha tsena li ka sebelisoa ho koetlisa mohlala oa ho ithuta mochini. Lintlha li tlameha ho kenyelletsa likarolo tse kang lebitso la sehlahisoa, thekiso, letsatsi la thekiso, le lintlha life kapa life tse amanang le tsona tse ka amang thekiso joalo ka lipapatso, matsatsi a phomolo kapa mekhoa ea sehla. 2. Ho sebetsa esale pele: Hloekisa data ho tlosa litekanyetso tse sieo kapa tse fosahetseng. Tsebetso ea pele ea data e boetse e kenyelletsa ho fetola data hore e be sebopeho se ka sebelisoang ke li-algorithms tsa ho ithuta ka mochini. 3. Khetho ea Litšobotsi: Khetha likarolo tse loketseng tse nang le kamano e matla le kholo ea thekiso ea lihlahisoa. 4. Khetho ea Mohlala: Khetha algorithm e loketseng ea ho ithuta mochini bakeng sa mosebetsi ona. Litaelo tse ling tse ka sebelisoang bakeng sa ho bolela esale pele thekiso li kenyelletsa Linear Regression, Lifate tsa Qeto, le Random Forest. 5. Koetliso: Koetlisa mokhoa oa ho ithuta oa mochine o sebelisa lintlha tsa khale tsa thekiso. Sena se kenyelletsa ho fepa algorithm ka likarolo tse peli tsa ho kenya letsoho le sepheo se fapaneng e leng kholo ea thekiso tabeng ena. 6. Tlhahlobo: Lekola tšebetso ea mmotlolo o koetlisitsoeng ka ho sebelisa metric e nepahetseng ea tlhahlobo. Sena se ka etsoa ka ho arola dataset hore e be setsi sa koetliso le tlhahlobo, ho koetlisa algorithm ho sete ea koetliso le ho lekola tsebetso ea eona sethaleng sa tlhahlobo. 7. Prediction: Hang ha mohlala o se o koetlisitsoe le ho hlahlojoa, o ka sebelisoa ho bolela esale pele hore na lihlahisoa li ka ba le kholo e kholo ka ho fetisisa ea thekiso kotareng e latelang. Ke habohlokoa ho hlokomela hore mefuta ea ho ithuta ka mochine e ka na ea hloka ho koetlisoa nako le nako ha lintlha tse ncha li ntse li fumaneha ho boloka ho nepahala ha tsona ho bolela esale pele kholo ea thekiso. 