<prompt> Ngola lenaneo la khomphieutha le ka lemohang bomenemene khoebong ea likarete tsa ho reka ka mokitlane.nan  <response>: Instruction in English: Write a computer program that can detect fraud in credit card transactions. . Response in English: Here is a sample Python program that can detect fraud in credit card transactions: Import necessary librariesimport pandas as pdfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import confusion_matrixfrom sklearn.preprocessing import StandardScaler Load the datasetdata pd.read_csvcreditcard.csv Preparing the dataX data.iloc:, :-1.valuesy data.iloc:, -1.valuessc StandardScalerX sc.fit_transformX Splitting the data into training and testing setsX_train, X_test, y_train, y_test train_test_splitX, y, test_size0.25, random_state0 Creating the random forest classifierclassifier RandomForestClassifiern_estimators100, random_state0classifier.fitX_train, y_train Predicting the resultsy_pred classifier.predictX_test Creating the confusion matrixcm confusion_matrixy_test, y_predprintConfusion Matrix:printcm Calculating the accuracy of the modelaccuracy cm00 cm11 sumsumcmprintAccuracy:, accuracyNote that this is a simplified version of a credit card fraud detection program. In practice, more advanced techniques such as anomaly detection and machine learning algorithms can be employed to improve the accuracy of fraud detection. Additionally, the program would need to be connected to a real-time data source for monitoring ongoing transactions. . Response in Sesotho: Mona ke mohlala oa lenaneo la Python le ka lemohang bomenemene litšebelisanong tsa likarete tsa mokoloto: Kenya lilaeborari tse hlokahalang kenya li-pandas joalo ka pd ho tsoa ho sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrixprecessering import sklearn. Laola dataset ea lintlha pd.read_csvcreditcard.csv Ho lokisa lintlha X data.iloc:, :-1.values y data.iloc:, -1.values sc StandardScaler X sc.fit_transformX Ho arola lintlha ho lihlopha tsa koetliso le liteko X_train, X_test, y_train, y_test train_test_splitX, y, test_size0.25, random_state0 Ho theha sehlopha sa morusu se sa reroang RandomForestClassifier n_estimators100, random_state0 classifier.fitX_train, y_train Ho bolela esale pele diphetho y_pred classifier.predictX_test Ho theha pherekano matrix cm confusion_matrixy_test, y_pred printConfusion Matrix: printcm Ho bala ho nepahala ha mohlala oa ho nepahala cm00 cm11 kakaretsosumcm printAccuracy:, accuracy Hlokomela hore ena ke mofuta o nolofalitsoeng oa lenaneo la ho lemoha bomenemene ka karete ea mokoloto. Ha e le hantle, mekhoa e tsoetseng pele e kang ho lemoha ka mokhoa o sa tloaelehang le mekhoa ea ho ithuta ka mochine e ka sebelisoa ho ntlafatsa ho nepahala ha ho lemoha bomenemene. Ho feta moo, lenaneo le tla hloka ho hokahana le mohloli oa data oa nako ea nnete bakeng sa ho beha leihlo litšebelisano tse tsoelang pele. 