input: Write a function to create a recommendation system in Python using Pandas that recommends new products to customers based on past purchases. The function should load data from a CSV file, create an item-based collaborative filter using the NearestNeighbors algorithm from scikit-learn, and print out recommendations for each product.
  assert recommend_products(data)
  # Expect output similar to:
  # Recommendations for item 0: [0, 1, 2]
  # Recommendations for item 1: [1, 2, 3]
  # Recommendations for item 2: [2, 3, 4]
  # Recommendations for item 3: [3, 4, 5]
  # Recommendations for item 4: [4, 5, 6]
output: def recommend_products(data):
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
      
      df = pd.read_csv(data)
      model_knn = NearestNeighbors(metric='cosine', algorithm='brute')
      model_knn.fit(df)
      
      for i in range(len(df)):
          distances, indices = model_knn.kneighbors([df.iloc[i]], n_neighbors=4)
          recommendations = indices.flatten()[1:]
          print(f"Recommendations for item {i}: {list(recommendations)}")