input: Write a function to create a machine learning program in Python that will classify text data into 3 different categories. The function should load the data using pandas, vectorize the text using CountVectorizer, convert target labels to numerical values, split the data into train and test sets, train a Multinomial Naive Bayes classifier, predict the categories using the test data, and print out the classification report. If the input file "data.csv" does not exist, the function should return False, otherwise it should return True after successfully printing the classification report.
  
  assert machine_learning_classification('data.csv') == True
  assert machine_learning_classification('nonexistentfile.csv') == False
output: def machine_learning_classification(filename):
      import pandas as pd
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import MultinomialNB
      from sklearn.metrics import classification_report
  
      try:
          data = pd.read_csv(filename)
      except FileNotFoundError:
          return False
  
      X = data['text']
      y = data['category']
  
      y = y.map({'category1': 0, 'category2': 1, 'category3': 2})
  
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(X)
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      classifier = MultinomialNB()
      classifier.fit(X_train, y_train)
  
      y_pred = classifier.predict(X_test)
  
      print(classification_report(y_test, y_pred))
  
      return True