Update README.md
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README.md
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# Assuming training and testing data are using the same names as we did in the skeleton code provided by the TA
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X_train = dataset_train['title']
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y_train = dataset_train['labels']
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X_test = dataset_test['title']
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y_test = dataset_test['labels']
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from sklearn.feature_extraction.text import TfidfVectorizer
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tfidf = TfidfVectorizer(max_features=5000, ngram_range=(1, 2), stop_words='english')
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X_train_tfidf = tfidf.fit_transform(X_train)
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X_test_tfidf = tfidf.transform(X_test)
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from sklearn.svm import SVC
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svm_model = SVC(kernel='linear', random_state=42)
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svm_model.fit(X_train_tfidf, y_train)
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y_pred = svm_model.predict(X_test_tfidf)
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accuracy = accuracy_score(y_test, y_pred)
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print(f"Random Forest Accuracy: {accuracy:.4f}")
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print(classification_report(y_test, y_pred))
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