Part 1: Frequent Pattern and Association 1. Implement an algorithm of your choice (Apriori, FP-Tree, ECLAT, etc.) to find frequent itemsets. Min-support should be a user input parameter. Two sample of transaction datasets are provided, Dataset1.dat and Dataset2.dat. 2. Extend your program to output all the maximal frequent itemsets and closed frequent itemsets. 3. Extend your program to output all the association rules. Min-Confidence should be a user input parameter. For all the association rules, output measures include confidence, lift, all- confidence and cosine similarity. Part 2. Use Amazon Web Services Machine Learning platform 1. Create an Amazon AWS account if you do not have it yet. 2. Learn to use Amazon Machine Learning platform for prediction. Use the sample dataset bank.csv if needed. 3. Use any data classification datasets you can find from the internet and use Amazon ML for classification and prediction. Write a short report (3 page maximal) explain what you did, what the results are, and what you have discovered and learned from this project. Part 1: Frequent Pattern and Association 1. Implement an algorithm of your choice (Apriori, FP-Tree, ECLAT, etc.) to find frequent itemsets. Min-support should be a user input parameter. Two sample of transaction datasets are provided, Dataset1.dat and Dataset2.dat. 2. Extend your program to output all the maximal frequent itemsets and closed frequent itemsets. 3. Extend your program to output all the association rules. Min-Confidence should be a user input parameter. For all the association rules, output measures include confidence, lift, all- confidence and cosine similarity. Part 2. Use Amazon Web Services Machine Learning platform 1. Create an Amazon AWS account if you do not have it yet. 2. Learn to use Amazon Machine Learning platform for prediction. Use the sample dataset bank.csv if needed. 3. Use any data classification datasets you can find from the internet and use Amazon ML for classification and prediction. Write a short report (3 page maximal) explain what you did, what the results are, and what you have discovered and learned from this project