input: Write a function to create a recommendation system in Python using Pandas, which recommends new products to customers based on their past purchases. The function should read product data from a CSV file, create an item-based collaborative filter using the NearestNeighbors algorithm, and print recommendations for each item.
  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])
  
  product_recommendation()
output: def product_recommendation():
      data = pd.read_csv('product_data.csv')
      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])