<prompt> Theha lenaneo ho Python ho hlophisa dataset e fanoengnan  <response>: Instruction in English: Create a program in Python to classify a given dataset . Response in English: Heres a sample program in Python that can be used to classify a given dataset:pythonfrom sklearn import treefrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_score sample dataset features: height, weight, shoe sizeX 181, 80, 44, 177, 70, 43, 160, 60, 38, 154, 54, 37, 166, 65, 40, 190, 90, 47, 175, 64, 39, 177, 70, 40, 159, 55, 37, 171, 75, 42, 181, 85, 43 labels: 0 for female, 1 for maleY 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1 splitting the dataset into the training set and test setX_train, X_test, y_train, y_test train_test_splitX, Y, test_size0.2, random_state42 create the classifierclf tree.DecisionTreeClassifierclf clf.fitX_train, y_train make a prediction using the test sety_pred clf.predictX_test calculate the accuracy scoreaccuracy accuracy_scorey_test, y_predprintfThe prediction accuracy is: accuracy100:.2fThis program uses the scikit-learn library to split the dataset into training and test sets, create a decision tree classifier, and to make a prediction and calculate the accuracy score. The features of the sample dataset represent the height, weight, and shoe size of a person, while the labels represent their gender. The classifier is trained on the training set and then makes predictions based on the test set. Finally, the accuracy score is calculated to determine how accurate the classifiers predictions are. . Response in Sesotho: Mona ke mohlala oa lenaneo ho Python le ka sebelisoang ho hlophisa dataset e fanoeng: python from sklearn import tree from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score sampole dataset likarolo: bolelele, boima, boholo ba seeta X 181, 80, 44, 177, 70, 43, 160, 60, 38, 154, 54, 37, 166, 65, 40, 190, 90, 47 , 175, 64, 39, 177, 70, 40, 159, 55, 37, 171, 75, 42, 181, 85, 43 labels: 0 bakeng sa basali, 1 bakeng sa male Y 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1 ho arola dataset ho sete ea koetliso le sete ea liteko X_train, X_test, y_train, y_test train_test_splitX, Y , test_size0.2, random_state42 bopa classifier clf sefate.DecisionTreeClassifier clf clf.fitX_train, y_train etsa boitsebelo o sebedisa sete ya teko y_pred clf.predictX_test bala ho nepahala lintlha tse nepahetseng accuracy_scorey_test, y_pred printfHo nepahala ha ponelopele ke: acuracy100:.2f Lenaneo lena le sebelisa laeborari ea scikit-lern ho arola dataset ka lithupelo le lihlopha tsa liteko, theha sehlopha sa sefate sa liqeto, le ho etsa ponelopele le ho bala lintlha tse nepahetseng. Litšobotsi tsa sampole ea dataset li emela bolelele, boima le boholo ba seeta sa motho, ha lileibole li emela bong ba bona. Mohlophisi o koetlisoa ka sete ea koetliso ebe o etsa likhakanyo ho latela sete ea liteko. Qetellong, lintlha tse nepahetseng li baloa ho fumana hore na likhakanyo tsa mohlophisi li nepahetse hakae. 