input: Write a function to create a recommendation system that suggests new products to customers based on their past purchases using Python and Pandas. The system should use an item-based collaborative filtering approach and make recommendations using the K-Nearest Neighbors algorithm. The input is a CSV file containing product data. The output should be the indices of recommended products for each item in the dataset.
  
  assert create_recommendation_system('product_data.csv')[0] == [2, 1, 3]
  assert create_recommendation_system('product_data.csv')[1] == [3, 2, 0]
  assert create_recommendation_system('product_data.csv')[2] == [1, 0, 3]
  assert create_recommendation_system('product_data.csv')[3] == [2, 0, 1]
output: 
  def create_recommendation_system(csv_file):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
      
      df = pd.read_csv(csv_file)
      model_knn = NearestNeighbors(metric='cosine', algorithm='brute')
      model_knn.fit(df)
      
      recommendations = []
      for i in range(len(df)):
          distances, indices = model_knn.kneighbors([df.iloc[i]], n_neighbors=4)
          recommendations.append(indices.flatten().tolist()[1:])
      
      return recommendations
  