#import pandas library import pandas as pd from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier from sklearn.model_selection import train_test_split # Import train_test_split function from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation col_names = [ 'pregnant' , 'glucose' , 'bp' , 'skin' , 'insulin' , 'bmi' , 'pedigree' , 'age' , 'label' ] pima = pd.read_csv( "diabetes.csv" , header = None , names = col_names) feature_cols = [ 'pregnant' , 'insulin' , 'bmi' , 'age' , 'glucose' , 'bp' , 'pedigree' ] x = pima[feature_cols] y = pima.label x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3 , random_state = 1 ) # Create Decision Tree classifer object clf = DecisionTreeClassifier( criterion = 'entropy' , max_depth = 3 ) # Train Decisi...
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