<prompt> Andika programu ya kompyuta inayoweza kutambua udanganyifu katika shughuli za kadi za mkopo.  <response>: Hapa ni mfano wa programu Python ambayo inaweza kugundua udanganyifu katika shughuli za kadi ya mkopo: Import muhimu maktaba kuagiza panda kama pd kutoka sklearn.ensemble import RandomForestClassifier kutoka sklearn.model_selection import train_test_split kutoka sklearn.metrics import confusion_matrix kutoka sklearn.preprocessing import StandardScaler Load the dataset data pd.read_csvcreditcard.csv Kuandaa data X data.iloc:,:-1.values y data.iloc:, -1.values y StandardScaler X sets sc.pred_transformX Splitting the data into and testing X_train, X_test, y_test, y_test_split from sklearn.metrics import confusion_matrix from sklearn.preprocessing import StandardScaler Load the dataset data pd.read_csvcreditcard.csv Kuandaa data X data.iloc:, -1.values y data.iloc, -1.values StandardScaler X sc.pred_transformX Splitting the data into and testing X_train_test, X_test_pred, y_test_split, y_test_split, y_test_matrix, y_conn02cm, y_printing_classifier Castclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassificationclassification 