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 in data from a specified CSV file, create an item-based collaborative filter using the NearestNeighbors algorithm from sklearn, and print out recommendations for each product.
  assert create_recommendation_system('product_data.csv')==None
output: def create_recommendation_system(csv_file):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
  
      data = pd.read_csv(csv_file)
      pivot_table = data.pivot_table(index='customer_id', columns='product_id', values='purchase_count')
      pivot_table = pivot_table.fillna(0)
  
      model_knn = NearestNeighbors(metric='cosine', algorithm='brute')
      model_knn.fit(pivot_table)
  
      for product in pivot_table.columns:
          product_series = pivot_table[product]
          distances, indices = model_knn.kneighbors([product_series.values], n_neighbors=6)
          recommendations = []
          for i in range(1, len(distances.flatten())):
              recommendations.append(pivot_table.columns[indices.flatten()[i]])
          print(f"Recommendations for product {product}: {recommendations}")