input: Write a python function to train a Naive Bayes model on email data and predict the classification of a given email.
  assert predict_email_classification('data.csv', "Holiday alert. Hi everyone, we have planned a holiday on 15th August.") in [0, 1]
  assert predict_email_classification('data.csv', "Meeting reminder. The meeting is scheduled for 10 AM tomorrow.") in [0, 1]
  assert predict_email_classification('data.csv', "You've won a free ticket to Bahamas!") in [0, 1]
output: def predict_email_classification(csv_file, email_text):
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.model_selection import train_test_split
      from sklearn.pipeline import make_pipeline
      
      # Load the data
      data = pd.read_csv(csv_file)
      
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(data['text'], data['label'], test_size=0.2, random_state=42)
      
      # Create a pipeline that vectorizes the data then applies a Naive Bayes classifier
      model = make_pipeline(CountVectorizer(), MultinomialNB())
      
      # Train the model
      model.fit(X_train, y_train)
      
      # Predict the classification of the given email text
      prediction = model.predict([email_text])[0]
      
      return prediction