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PRACTICAL NO.1 AIM: Perform the data classification using classification algorithm. 1A. Perform the data classification using Naïve Baye’s Algorithm. from sklearn.datasets import load_iris iris = load_iris() X = iris.data y = iris.target from sklearn.model_selection import train_test_split X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4,random_state=1) from sklearn.naive_bayes import GaussianNB gnb=GaussianNB() gnb.fit(X_train,y_train) y_pred=gnb.predict(X_test) from sklearn import metrics print("Gaussian Naive Bayes model accuracy(in%):",metrics.accuracy_score(y_test,y_pred)*100) EXAMPLE-span.csv import numpy as np import pandas as pd from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB df=pd.read_csv('/content/spam1.csv',encoding='latin-1') df=df[['Message','Category']] df.columns=['SMS','Type'] countvec=CountVectorizer(ngram_range=(1,4),stop_words='eng...
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