Exp 11

11. Write a program for Naive Bayesian Classification in Python import pandas as pd import numpy as np from sklearn import datasets iris = datasets.load_iris() # importing the dataset iris.data # showing the iris data X=iris.data #assign the data to the X y=iris.target #assign the target/flower type to the y print (X.shape) print (y.shape) fromsklearn.model_selection import train_test_split X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=9) fromsklearn.naive_bayes import GaussianNB nv = GaussianNB() # create a classifier nv.fit(X_train,y_train) # fitting the data fromsklearn.metrics import accuracy_score y_pred = nv.predict(X_test) # store the prediction data accuracy_score(y_test,y_pred) # calculate the accuracy