input: Write a python function to load a dataset, clean it, extract features, split it into training and testing sets, build a Multinomial Naive Bayes classifier, and evaluate the model's performance. 
  assert my_spam_classifier('spam_dataset.csv') > 0.8
output: def my_spam_classifier(file_path):
      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 accuracy_score
  
      # Load dataset
      data = pd.read_csv(file_path)
      
      # Clean dataset (Assuming 'text' column for messages and 'label' column for spam/ham)
      data.dropna(inplace=True)  # Drop missing values
      data = data[data['text'].str.strip().astype(bool)]  # Remove rows with empty 'text'
  
      # Extract features
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data['text'])
      y = data['label']
  
      # Split into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Build Multinomial Naive Bayes classifier
      classifier = MultinomialNB()
      classifier.fit(X_train, y_train)
  
      # Evaluate the model's performance
      y_pred = classifier.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy