<prompt> Theha mokhoa oa marang-rang oa neural o khonang ho bolela esale pele theko ea theko ea nakong e tlang e thehiloeng ho data ea histori ea theko ea thepa.Mohlala oa data ea nalane: 20,19.5,23,22.5,25,24.5  <response>: Instruction in English: Construct a neural network model capable of predicting the future stock price based on historical stock price data. . Response in English: To construct a neural network model that can predict future stock prices based on historical data, youll need to follow several steps:1. Gather and preprocess your data: historical stock data here, time-series analysis techniques will work well to preprocess the data. An example of data preprocessing could be data normalization where the data is transformed to have zero mean and unit variance2. Define your model: there are several types of neural networks that work well with time-series data. For stock prediction, a popular approach is to use Recurrent Neural Networks RNN or Long Short-Term Memory LSTM models. Here is an example of how to define a simple LSTM model:pythonfrom tensorflow.keras.models import Sequentialfrom tensorflow.keras.layers import LSTM, Dense Set the sequence lengthseq_length 6 Build the modelmodel Sequentialmodel.addLSTM50, input_shapeseq_length, 1model.addDense1 Compile the modelmodel.compilelossmean_squared_error, optimizeradam3. Train your model: Now that you have your data and defined your model, its time to train it. You will have to split your data into training and test sets and then use the training set to train your model. Here is an example of how to prepare data and train the model:pythonimport numpy as np Import the datadata 20, 19.5, 23, 22.5, 25, 24.5 Scale the datadata np.arraydata.reshape-1, 1data data - data.min data.max - data.min Prepare data for the modelX y for i in rangeseq_length, lendata: X.appenddatai - seq_length:i, 0 y.appenddatai, 0X np.arrayXy np.arrayyX np.reshapeX, X.shape0, X.shape1, 1 Train the modelmodel.fitX, y, epochs100, batch_size104. Make your predictions: After training the model, you can now use it to make predictions about the future stock prices. Here is an example of how to use the trained model to make a prediction:python Predict the next value . Response in Sesotho: Ho aha mohlala oa neural network o ka bolelang esale pele litheko tsa nakong e tlang ho latela data ea nalane, o tla hloka ho latela mehato e maloa: 1. Bokella le ho lokisa data ea hau esale pele: data ea nalane ea setoko mona, mekhoa ea tlhahlobo ea nako e tla sebetsa hantle ho lokisa esale pele. data. Mohlala oa data preprocessing e ka ba ho tloaeleha ha data moo data e fetoloang ho ba le zero mean le unit variance 2. Hlalosa mohlala oa hau: ho na le mefuta e mengata ea marang-rang a marang-rang a sebetsang hantle ka lintlha tsa nako. Bakeng sa ho bolela esale pele ka setoko, mokhoa o tsebahalang ke oa ho sebelisa mefuta ea Recurrent Neural Networks RNN kapa ea Long Short-Term Memory LSTM. Mona ke mohlala oa mokhoa oa ho hlalosa mohlala o bonolo oa LSTM: python ho tloha tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense Seta bolelele ba tatelano seq_length 6 Haha mohlala oa mohlala Sequential mohlala.eketsaLSTM50, input_shapeseq_length, 1 model.addDense1 Kopanya mohlala.kopanyatahlehelomean_squared_error, optimizeradam 3. Koetlisa mohlala oa hau: Kaha joale u na le data ea hau me u hlalositse mohlala oa hau, ke nako ea ho e koetlisa. U tla tlameha ho arola lintlha tsa hau ka lithupelo le lihlopha tsa liteko ebe u sebelisa sete ea koetliso ho koetlisa mohlala oa hau. Mona ke mohlala oa mokhoa oa ho lokisa data le ho koetlisa mohlala: python import numpy as np Kenya data data 20, 19.5, 23, 22.5, 25, 24.5 Scale the data np.array data.reshape-1, 1 data data - data.min data.max - data.min Lokisetsa lintlha tsa mohlala X y bakeng sa ke ka bopharaseq_length, lendata: X.appenddatai - seq_length:i, 0 y.appenddatai, 0 X np.arrayX y np .arrayy X np.reshapeX, X.shape0, X.shape1, 1 Koetlisa mohlala oa mohlala.fitX, y, epochs100, batch_size10 4. Etsa likhakanyo tsa hau: Kamora ho koetlisa mohlala, joale u ka o sebelisa ho etsa likhakanyo mabapi le litheko tsa nakong e tlang. Mona ke mohlala oa mokhoa oa ho sebelisa mohlala o koetlisitsoeng ho bolela esale pele: python Predict boleng bo latelang 