Please help with this Weka problem.

The marketing department of a financial firm keeps records on customers, including demographic information and the type of accounts. When launching a new product, such as a "Personal Equity Plan" (PEP), a direct mail piece, advertising the product, is sent to existing customers, and a record kept as to whether that customer responded and bought the product. Based on this store of prior experience, the managers decide to use data mining techniques to build customer profile models. In this problem we are interested only in deriving (quantitative) association rules from the data. The data contains the following fields:

id	a unique identification number
age	age of customer in years (numeric)
sex	MALE / FEMALE
region	inner_city/rural/suburban/town
income	income of customer (numeric)
married	is the customer married (YES/NO)
children	number of children (numeric)
car	does the customer own a car (YES/NO)
save_acct	does the customer have a saving account (YES/NO)
current_acct	does the customer have a current account (YES/NO)
mortgage	does the customer have a mortgage (YES/NO)
pep	did the customer buy a PEP (Personal Equity Plan) after the last mailing (YES/NO)

The data is contained in the file bank-data.csv. Each record is a customer description where the "pep" field indicates whether that customer bought a PEP after the last mailing.

Your goal is to perform Association Rule discovery on the data set using the Weka package.

Note: Association rule mining requires discretization of continuous variables. This task can be performed in the data transformation step or (in some cases) by the mining program. WEKA is a full data mining suite which includes various preprocessing modules (filters). When using WEKA, you will first apply the relevant preprocessing filters to transform the data before you perform association rule discovery.

First perform the necessary preprocessing steps required for association rule mining. Specifically, the "id" field will need to be removed and the numerical attributes must be discretized.

1. (2 points) Now perform association rule discovery on the transformed data. Experiment with different parameters so that you get at least 20-30 strong rules (e.g., rules with high lift and confidence which at the same time have relatively good support). In WEKA Apriori algorithm interface set "outputItemsets" to "True" so that you can also view the frequent items sets of different sizes in addition to the rules. Indicate what those strong rules are.
2. (3 points) Select the top 5 most "interesting" rules and for each specify the following:
3. 1. an explanation of the pattern and why you believe it is interesting based on the business objectives of the company,
4. 2. any recommendations based on the discovered rule that might help the company to better understand behavior of its customers or in its marketing campaign.

5. Note: The top 5 most interesting rules are most likely not the top 5 in the result set of the Apriori algorithm. They are rules that, in addition to having high support, lift, and confidence, also provide some non-trivial, actionable knowledge based on the underlying business objectives.