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 using the NearestNeighbors algorithm from scikit-learn, and make recommendations for each item in the dataset. The results should be printed out.
  
  def recommend_products(file_path):
   import pandas as pd
   from sklearn.neighbors import NearestNeighbors
   
   # Read in data
   data = pd.read_csv(file_path)
   
   # 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])
    
  # Example usage
  recommend_products('product_data.csv')
output: def recommend_products(file_path):
   import pandas as pd
   from sklearn.neighbors import NearestNeighbors
   
   # Read in data
   data = pd.read_csv(file_path)
   
   # 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])