input: 
  def classify_emails_as_spam_or_not(dataset_path: str) -> float:
      """ Create a machine learning model to classify emails as spam or non-spam using the Enron Spam Dataset.
      The model uses a linear Support Vector Machine (SVM) and evaluates the precision score of the classification.
      >>> classify_emails_as_spam_or_not("spam.csv")
      0.95
      >>> classify_emails_as_spam_or_not("invalid_path.csv")
      0.0
      """
      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
  
      try:
          # 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
      except Exception as e:
          print(f"An error occurred: {e}")
          return 0.0
  
output:     try:
          # 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
      except Exception as e:
          print(f"An error occurred: {e}")
          return 0.0