ML Lab 6. Exp (sklearn decision tree)
import pandas as pd from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split from sklearn import metrics col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label'] pima = pd.read_csv("/content/diabetes.csv", header=None, names=col_names) pima.head() #Feature Selection #split dataset in features and target variable 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) #Building Decision Tree Model clf = DecisionTreeClassifier() clf = clf.fit(X_train,y_train) y_pred = clf.predict(X_test) #Evaluating the Model print("Accuracy:",metrics.accuracy_score(y_test, y_pred)) #Accuracy: 0.658008658008658 #Optimizing Deci...