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Below is an instruction that describes a task. Write a respo...

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Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Create an AI model in Python to classify emails from the Enron Spam Dataset as either spam or non-spam. ### Input: Not applicable ### Output: import pandas as pd from sklearn.feature_extraction.text import CountVectorizer from sklearn.model_selection import train_test_split from sklearn import svm from sklearn.metrics import precision_score # Load the dataset df = pd.read_csv("spam.csv", encoding='iso-8859-1') # Load label and data X = df['EmailText'] y = df['Label'] # Split data into train-test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42) # Create bag of words model count_vectorizer = CountVectorizer() X_train_counts = count_vectorizer.fit_transform(X_train) # Train the model clf = svm.SVC(kernel='linear') clf.fit(X_train_counts, y_train) # Test the model X_test_counts = count_vectorizer.transform(X_test) predictions = clf.predict(X_test_counts) score = precision_score(y_test, predictions, average='binary') print('Precision score of the model:', score)

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Hugging Face

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