<prompt> Theha neural network e bolelang esale pele monyetla oa hore moreki a reke sehlahisoa.nan  <response>: Instruction in English: Construct a neural network that predicts the probability of a customer buying a product. . Response in English: To build a neural network model that can predict the probability of a customer buying a product, youll need to follow these steps:1. Collect and clean data: Youll need to gather data about the customers buying habits, demographics, and behaviors. Clean the data to remove any inconsistencies or missing values.2. Preprocess data: Split the data into training and testing sets. Normalize or standardize the data to ensure the features have the same scale.3. Build a neural network model: Create a neural network architecture with an input layer, one or more hidden layers, and an output layer. The input layer should have as many nodes as there are features, and the output layer should have a single node for binary classification.4. Compile the model: Choose a loss function, such as binary cross-entropy, and an optimization algorithm, such as stochastic gradient descent SGD.5. Train the model: Feed the training data into the model and adjust the weights and biases of the network through backpropagation.6. Evaluate the model: Use the test data to evaluate the models performance. Calculate metrics such as accuracy, precision, and recall to determine how well the model can predict the probability of a customer buying a product.7. Use the model to make predictions: Use the trained and validated model to make predictions on new data. The output layer will provide a probability value between 0 and 1, representing the likelihood of the customer buying the product.Keep in mind that the performance of the model will depend on the quality of the data and the chosen architecture of the neural network. It is always a good idea to experiment with different hyperparameters to find a model that performs well on your specific data. . Response in Sesotho: Ho aha mohlala oa neural network o ka bolelang esale pele monyetla oa hore moreki a reke sehlahisoa, o tla hloka ho latela mehato ena: 1. Bokella le ho hloekisa data: U tla hloka ho bokella lintlha mabapi le mekhoa ea bareki ea ho reka, palo ea batho, le maitshwaro. Hloekisa lintlha ho tlosa ho se lumellane hofe kapa hofe kapa litekanyetso tse sieo. 2. Lintlha tsa Preprocess: Arola lintlha ho lihlopha tsa koetliso le liteko. Fetola kapa u ntlafatse datha ho netefatsa hore likarolo li na le sekala se lekanang. 3. Haha mohlala oa marang-rang oa neural: Theha mohaho oa marang-rang oa neural o nang le lera le kenyang, lesela le le leng kapa ho feta le patiloeng, le lera la tlhahiso. Lera la ho kenya le lokela ho ba le li-node tse ngata kamoo ho nang le likarolo, me karolo ea lihlahisoa e lokela ho ba le node e le ngoe bakeng sa lihlopha tsa binary. 4. Kopanya mohlala: Khetha mosebetsi oa tahlehelo, joalo ka binary cross-entropy, le algorithm ea optimization, joalo ka stochastic gradient descent SGD. 5. Koetlisa mohlala: Fepa lintlha tsa koetliso ho mohlala le ho fetola litekanyo le likhetho tsa marang-rang ka ho khutlela morao. 6. Hlahlobisisa mohlala: Sebelisa lintlha tsa teko ho hlahloba tsebetso ea mohlala. Bala lipalo tse kang ho nepahala, ho nepahala, le ho hopola ho fumana hore na mohlala o ka bolela esale pele ka monyetla oa hore moreki a reke sehlahisoa. 7. Sebelisa mohlala ho etsa likhakanyo: Sebelisa mohlala o koetlisitsoeng le o tiisitsoeng ho etsa likhakanyo ka lintlha tse ncha. Karolo e hlahisoang e tla fana ka boleng ba monyetla pakeng tsa 0 le 1, e emelang monyetla oa hore moreki a reke sehlahisoa. Hopola hore tsebetso ea mohlala e tla itšetleha ka boleng ba data le mohaho o khethiloeng oa neural network. Kamehla ke mohopolo o motle ho leka li-hyperparameter tse fapaneng ho fumana mohlala o sebetsang hantle ho data ea hau e ikhethang. 