input: Write a function that creates a recommendation system in Python using Pandas to recommend new products to customers based on past purchases. The function should read a CSV file containing product data, create an item-based collaborative filter, and generate recommendations for each product.
  
  import pandas as pd
  from sklearn.neighbors import NearestNeighbors
  
  def product_recommendation():
      # Read in data
      data = pd.read_csv('product_data.csv')
  
      # 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() is None # The function should run without errors and print recommendations.
output: 
  def product_recommendation():
      # Read in data
      data = pd.read_csv('product_data.csv')
  
      # 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])
  