input: Write a function that loads the Enron Spam Dataset, preprocesses the email text using a bag of words model, trains a linear SVM classifier on the training data, and evaluates its precision on the test data. The function should return the precision score of the model. The dataset should be loaded from a CSV file named "spam.csv".
  
  def spam_classifier_precision():
      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')
      
      return score
  
  assert spam_classifier_precision()==0.98
  assert spam_classifier_precision()==0.97
  assert spam_classifier_precision()==0.99
  assert spam_classifier_precision()==0.96
output: def spam_classifier_precision():
      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
      
      df = pd.read_csv("spam.csv", 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')
      
      return score