Scikit Learn

Logistic Regression Is used for classification problems, outputs probabilities if > 0.5 the data is labeled 1 else it is labeled 0. Produces a linear decision boundary. from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split logreg = LogisticRegression() X_train, X_text, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=42) logreg.fit(X_train,y_train) y_pred= logreg.predict(X_test) ROC Curve Stands for Receiver Operating Characteristics curve…