input: 
  
  def classify_emails_as_spam(dataset_path: str) -> float:
      """
      This function loads the Enron Spam Dataset from a given CSV file, preprocesses the emails using a bag of words model, 
      trains a Support Vector Machine classifier, and evaluates the model's precision in classifying emails as either spam or non-spam.
      >>> classify_emails_as_spam("spam.csv")
      # Output will be a float representing 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
  
      # 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')
  
      return score
  
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(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')
  
      return score