figure example training (circlesand test (squaresset for two class classification explanatory variables are the (xycoordinatesclasses are zero (greenor one (bluefrom sklearn import preprocessing min_max_scaler preprocessing minmaxscaler feature_range =( )x_scaled min_max_scaler fit_transform (xequivalent tox_scaled ( min(axis = )( max(axis = min(axis = ) fitting and prediction once the data has been partitioned and standardized if necessarythe data may be fitted to statistical modele classification or regression model for examplecontinuing with our data from abovethe following fits model to the data and predicts the responses for the test set from sklearn somesubpackage import someclassifier clf someclassifier (choose appropriate classifier clf fit(x_train y_train fit the data y_prediction clf predict x_test predict specific classifiers for logistic regressionnaive bayeslinear and quadratic discriminant analysisk-nearest neighborsand support vector machines are given in section testing the model once the model has made its prediction we may test its effectivenessusing relevant metrics for examplefor classification we may wish to produce the confusion matrix for the