41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
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#!/usr/bin/env python3
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import numpy as np
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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 classification_report
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#Read the email dataset.
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data = pd.read_csv('emails_dataset.csv')
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df = pd.DataFrame(data)
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#Convert text to numbers.
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vectorizer = CountVectorizer()
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X = vectorizer.fit_transform(df['Message'])
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#Split the dataset for training and testing
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y = df['Classification']
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
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#Train the model
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clf = MultinomialNB()
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clf.fit(X_train, y_train)
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#Evaluate the model's performance
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y_pred = clf.predict(X_test)
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print(classification_report(y_test, y_pred))
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#Add a simple test to the model
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message = vectorizer.transform(["Today's Offer! Claim ur $150 worth of discount vouchers! Text YES to 85023 now! SavaMob, member offers mobile! T Cs 08717898035. $3.00 Sub. 16 . Unsbub reply X"])
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prediction = clf.predict(message)
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print("The email is: ", prediction[0])
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#Run the complete test of the emails inside "test_emails.csv"
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test_data = pd.read_csv("test_emails.csv")
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X_new = vectorizer.transform(test_data['Messages'])
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new_predictions = clf.predict(X_new)
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results_df = pd.DataFrame({'Messages': test_data['Messages'], 'Prediction': new_predictions})
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print(results_df)
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