input: Write a function to classify emails from the Enron Spam Dataset as either spam or non-spam using a machine learning model. The function should load the dataset, preprocess the text data, train a Support Vector Machine (SVM) classifier, and evaluate its precision score. The function should return the precision score of the model.
  
  assert classify_emails() > 0.8
output: def classify_emails():
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.svm import SVC
      from sklearn.metrics import precision_score
  
      # Load the dataset
      df = pd.read_csv('enron_spam_data.csv')
      df['label'] = df['label'].map({'ham': 0, 'spam': 1})
  
      # Preprocess the text data
      X = df['text']
      y = df['label']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Vectorize the text data
      vectorizer = TfidfVectorizer()
      X_train_vec = vectorizer.fit_transform(X_train)
      X_test_vec = vectorizer.transform(X_test)
  
      # Train the SVM classifier
      svm = SVC(kernel='linear')
      svm.fit(X_train_vec, y_train)
  
      # Evaluate the model
      y_pred = svm.predict(X_test_vec)
      precision = precision_score(y_test, y_pred)
  
      return precision