input: Write a function to create an AI model in Python that classifies emails from the Enron Spam Dataset as either spam or non-spam. The function should load the dataset, split it into train-test sets, create a bag of words model, train a Support Vector Machine (SVM) classifier, and print the precision score of the model.
  
  
  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
  
  def classify_enron_spam(dataset_path):
      # Load the dataset
      df = pd.read_csv(dataset_path, 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)
  
  # Example usage:
  assert classify_enron_spam("spam.csv") is None
  
output: def classify_enron_spam(dataset_path):
      df = pd.read_csv(dataset_path, encoding='iso-8859-1')
      X = df['EmailText']
      y = df['Label']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)
      count_vectorizer = CountVectorizer()
      X_train_counts = count_vectorizer.fit_transform(X_train)
      clf = svm.SVC(kernel='linear')
      clf.fit(X_train_counts, y_train)
      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)