input: Write a python function to train a Random Forest classifier on loan data and calculate the accuracy.
  assert train_random_forest(data) >= 0.70  # Example accuracy threshold
  assert isinstance(train_random_forest(data), float)
output: def train_random_forest(data):
      from sklearn.model_selection import train_test_split
      from sklearn.ensemble import RandomForestClassifier
      from sklearn.metrics import accuracy_score
  
      # Assuming the last column is the target variable
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      # Split the data 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)
  
      # Initialize the Random Forest classifier
      clf = RandomForestClassifier(random_state=42)
  
      # Train the classifier
      clf.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = clf.predict(X_test)
  
      # Calculate accuracy
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy