Python Logistic Regression Model Implementation and Performance Evaluation

'lrmodel = LogisticRegression(penalty= 'l2'):' This line of code creates a logistic regression model with L2 regularization.

'start=datetime.datetime.now():' This line of code records the start time of the model training.

'lrmodel.fit(x_train, y_train):' This line of code fits the logistic regression model to the training data (x_train and y_train).

'end=datetime.datetime.now():' This line of code records the end time of the model training.

'Ypred_lr=lrmodel.predict(x_test):' This line of code predicts the target variable (y) using the logistic regression model and the test data (x_test).

'print(" confusion_matrix "):' This line of code prints the string "confusion_matrix".

'print(metrics.confusion_matrix(y_test,ypred_lr)):' This line of code prints the confusion matrix between the true target variable (y_test) and the predicted target variable (ypred_lr).

'print(' Accuracy:%f'%(metrics.accuracy_score(y_test,ypred_lr))):' This line of code prints the accuracy of the logistic regression model on the test data.

'print(' Area under the curve (AUC):%f'%(metrics.roc_auc_score(y_test,ypred_lr))):' This line of code prints the area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the logistic regression model on the test data.

'print(' Runtime = ',end-start):' This line of code prints the runtime of the model training.