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 product data from a CSV file, create an item-based collaborative filter using NearestNeighbors, and print recommendations for each product.
  import pandas as pd
  from sklearn.neighbors import NearestNeighbors
  
  def product_recommendation_system(csv_file):
      # Read in data
      data = pd.read_csv(csv_file)
      
      # Create an item-based collaborative filter
      cf = NearestNeighbors(metric='cosine', algorithm='brute')
      cf.fit(data)
      
      # Make recommendations
      distances, indices = cf.kneighbors(data, n_neighbors=3)
      
      # Print results 
      for i in range(len(data)):
          print('Recommendations for item {}:'.format(i), indices[i])
  
  assert product_recommendation_system('sample_product_data.csv') is None
output: def product_recommendation_system(csv_file):
      data = pd.read_csv(csv_file)
      cf = NearestNeighbors(metric='cosine', algorithm='brute')
      cf.fit(data)
      distances, indices = cf.kneighbors(data, n_neighbors=3)
      for i in range(len(data)):
          print('Recommendations for item {}:'.format(i), indices[i])