input: Write a function to create a recommendation system using Pandas in Python that recommends new products to customers based on their past purchases. The function should read data from a CSV file, create an item-based collaborative filter using the NearestNeighbors algorithm from sklearn, and then print recommendations for each item in the dataset.
  assert recommendation_system() == ["Recommendations for item 0: [0 1 2]", "Recommendations for item 1: [1 0 2]", "Recommendations for item 2: [2 0 1]"]
output: def recommendation_system():
      import pandas as pd
      from sklearn.neighbors import NearestNeighbors
      
      data = pd.read_csv('data.csv')
      model = NearestNeighbors(metric='cosine', algorithm='brute')
      model.fit(data)
      
      recommendations = []
      for i in range(len(data)):
          distances, indices = model.kneighbors([data.iloc[i]], n_neighbors=3)
          recommendations.append(f"Recommendations for item {i}: {indices.flatten().tolist()}")
      
      return recommendations