PROG6

 import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.naive_bayes import GaussianNB

from sklearn import metrics

df=pd.read_csv("pima_indian.csv")

feature_col_names=['num_preg','glucose_conc','diastolic_bp','thickness','insulin','bmi','diab_pred','num_preg','age']

predicted_class_names=['diabetes']

x=df[feature_col_names].values

y=df[predicted_class_names].values

xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.33)

print('\n total numbe of training data:',ytrain.shape)

print('\n total numbe of test data:',ytest.shape)

clf=GaussianNB().fit(xtrain,ytrain.ravel())

predicted=clf.predict(xtest)

predictTestData=clf.predict([[16,146,72,35,0,33.6,0.627,50,1]])

print('\n Confusion_matrix:',metrics.confusion_matrix(ytest,predicted))

print('\n accuracy of classifier:',metrics.accuracy_score(ytest,predicted))

print('\n the value of precision:',metrics.precision_score(ytest,predicted))

print('\n the value of recall:',metrics.recall_score(ytest,predicted))

print('\n predicted value of individual test data:',predictTestData)