python
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
import pickle
data = pd.read_csv('spam_dataset.csv')
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(data['content'])
y = data['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
classifier = MultinomialNB()
classifier.fit(X_train, y_train)
with open('classifier.pkl', 'wb') as file:
pickle.dump(classifier, file)
python
import pickle
from sklearn.feature_extraction.text import CountVectorizer
with open('classifier.pkl', 'rb') as file:
classifier = pickle.load(file)
with open('vectorizer.pkl', 'rb') as file:
vectorizer = pickle.load(file)
email_vector = vectorizer.transform([email_content])
prediction = classifier.predict(email_vector)
if prediction[0] == 1:
else:
