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