input: Write a function to create a recommendation system in Python using Pandas to recommend new products to customers based on past purchases. The function should read data from a CSV file named 'product_data.csv', create an item-based collaborative filter using the NearestNeighbors algorithm, and then make recommendations for each product in the dataset. If successful, the function should print the recommendations for each product.
  assert create_recommendation_system()
  
  # Example output:
  # Recommendations for item 0: [1 3 2]
  # Recommendations for item 1: [0 2 4]
  # Recommendations for item 2: [1 3 0]
  # ...
output: def create_recommendation_system():
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
  
      data = pd.read_csv('product_data.csv')
      model = NearestNeighbors(metric='cosine', algorithm='brute')
      model.fit(data)
  
      for i in range(len(data)):
          distances, indices = model.kneighbors([data.iloc[i]], n_neighbors=4)
          recommendations = indices[0][1:]
          print(f"Recommendations for item {i}: {recommendations}")