input: Write a python function to load the 'iris.csv' dataset, split the data into training and testing sets, create and train a KNeighborsClassifier model, predict classifications, and calculate the accuracy. 
  assert classify_iris_data('iris.csv') > 0.9
  assert isinstance(classify_iris_data('iris.csv'), float)
output: def classify_iris_data(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.neighbors import KNeighborsClassifier
      from sklearn.metrics import accuracy_score
      
      # Load the dataset
      df = pd.read_csv(file_path)
      
      # Split into features and target
      X = df.drop('species', axis=1)
      y = df['species']
      
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
      
      # Create and train the KNeighborsClassifier model
      knn = KNeighborsClassifier(n_neighbors=3)
      knn.fit(X_train, y_train)
      
      # Predict classifications
      y_pred = knn.predict(X_test)
      
      # Calculate the accuracy
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy