input: Write a function to implement a recommendation system in Python using Pandas to recommend new products to customers based on past purchases. The recommendation system should utilize the NearestNeighbors algorithm from sklearn to find similar products. The function should read the product data from a CSV file, create a collaborative filter, and provide recommendations for each item in the dataset. If successful, the function should print the recommendations for each item.
  
  import pandas as pd
  from sklearn.neighbors import NearestNeighbors
  
  def recommend_products(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 recommend_products('product_data.csv') is None
output: 
  def recommend_products(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])
  