<prompt> Theha mokhoa o sa reroang oa ho fokotseha ha meru ho bolela esale pele litheko tsa ntlo ho latela lintlha tse fanoeng.Lintlha nomoro, boholo, Balcony, Bedroom, Distance_Train, Cost, Outlay: 3602, 3, 1,3,17Km, 207000,6380 2375, 2, 1,2,27Km, 152000,3900 2, 360, 2, 360 ,21Km, 55000,4180 1800, 4, 2,3,30Km, 115000,5350  <response>: Instruction in English: Build a random forest regression model to predict the house prices based on the the given data. . Response in English: In order to build a random forest regression model to predict house prices, we need to organize the data and perform the following steps:1. Import the necessary libraries:pythonfrom sklearn.ensemble import RandomForestRegressorfrom sklearn.model_selection import train_test_splitimport numpy as np2. Organize and prepare the data:python First, we convert the input data into the right formatdata np.array3602, 3, 1, 3, 17, 207000, 6380, 2375, 2, 1, 2, 27, 152000, 3900, 1360, 2, 1, 2, 21, 55000, 4180, 1800, 4, 2, 3, 30, 115000, 5350 Then, we split the data into features and labelsX data:, :-1 all columns except the last oney data:, -1 the last column And now, we split our data into training and testing setsX_train, X_test, y_train, y_test train_test_splitX, y, test_size0.25, random_state03. Build the model:python Create the random forest regressor objectrf RandomForestRegressorn_estimators100, random_state0 Train the model using the training setrf.fitX_train, y_train4. Evaluate the model:python Predict the house prices using the test sety_pred rf.predictX_test Evaluate the model using the mean absolute errorfrom sklearn.metrics import mean_absolute_errormae mean_absolute_errory_test, y_predprintfMean absolute error: maeNote: This is just a simple example of how to build a random forest regression model using a small dataset. For more accurate predictions, it is recommended to use a larger and more representative dataset, as well as fine-tuning the models hyperparameters. . Response in Sesotho: Bakeng sa ho aha mokhoa o sa reroang oa ho putlama ha meru ho bolela esale pele litheko tsa matlo, re hloka ho hlophisa lintlha le ho etsa mehato e latelang: 1. Kenya lilaebrari tse hlokahalang: python ho tsoa sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import train_test_split import numpy e le np 2. Hlophisa le ho lokisa lintlha: python Ntlha ea pele, re fetolela data e kentsoeng ka mokhoa o nepahetseng oa data np.array3602, 3, 1, 3, 17, 207000, 6380, 2375, 2, 1, 2, 27, 152000, 3900, 1360, 2, 1, 2, 21, 55000, 4180, 1800, 4, 2, 3, 30, 0, 50 11 Joale, re arola lintlha ka likarolo le lileibole X data:, :-1 litšiea tsohle ntle le ea ho qetela y data:, -1 kholomo ea ho qetela Me joale, re arola rona Lintlha mabapi le lihlopha tsa koetliso le liteko X_train, X_test, y_train, y_test train_test_splitX, y, test_size0.25, random_state0 3. Theha mohlala: python Theha ntho e sa reroang ea moru rf RandomForestRegressorn_estimators100, random_state0 Koetlisa mohlala u sebelisa sete ea koetliso rf.fitX_train, y_train 4. Hlahlobisisa mohlala: python Lekanya litheko tsa ntlo u sebelisa sete ea liteko y_pred rf.predictX_test Lekola mohlala ka ho sebelisa phoso e felletseng ho tsoa ho sklearn.metrics import mean_absolute_error mae mean_absolute_errory_test, y_pred printfBolela phoso e felletseng: mae Tlhokomeliso e mpa e le mohlala o bonolo oa mokhoa oa ho aha mohlala o sa reroang oa ho khutlisa meru ho sebelisa dataset e nyane. Bakeng sa likhakanyo tse nepahetseng haholoanyane, ho khothaletsoa ho sebelisa dataset e kholoanyane le e emelang haholoanyane, hammoho le ho lokisa li-hyperparameter tsa mohlala. 