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# -*- coding: utf-8 -*-
"""epurethim.159
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1UmqJMfDY_e89v6dh4maq9aobuj5vz79k
"""
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
from sklearn.metrics import accuracy_score
dataset = pd.read_csv('/content/emails.csv')
dataset.head()
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(dataset['text'])
X_train, X_test, y_train, y_test = train_test_split(X, dataset['spam'], test_size=0.2)
model = MultinomialNB()
model.fit(X_train, y_train)
yPred = model.predict(X_test)
accuracy = accuracy_score(y_test, yPred)
print(accuracy)
def predictMessage(message):
messageVector = vectorizer.transform([message])
prediction = model.predict(messageVector)
return 'Spam' if prediction[0] == 1 else 'Ham'
userMessage = input('Enter text to predict:')
prediction = predictMessage(userMessage)
print(f'The message is {prediction}') |