The data represents demographic information of the people contacted by the bank and whether they have subscribed to a term deposit or not. There are 45,211 instances and 12 attributes. The attribute descriptions are available below.

Assuming the role of one of the executives of this bank,task is to identify those customers that are most likely to subscribe to a term deposit.use Weka to analyze the data for identification of association rules. Load the data set in Weka and perform association rule mining. make assumptions when pre-processing the data for converting numeric attributes to nominal ones.

include the top 10 association rules generated by Weka (including support/confidence numbers). use the default settings or modify the rule generation parameters to improve the prediction accuracy.

Interpret findings by explaining a few of the top rules. Please document pre-processing decisions and assumptions too.

Summary data set description:

age: Age of the client. Numeric.

job: Client's job. Categorical. One of "admin.", "unknown", "unemployed", "management", "housemaid",

"entrepreneur", "student", "blue-collar", "self-employed", "retired", "technician", "services"

marital: Marital status. Categorical. One of "married", "divorced", "single"

education: Education level. Categorical. One of "unknown", "secondary", "primary", "tertiary"

default: Whether the client defaulted on credit. Binary. "yes", "no"

balance: Average yearly balance. Numeric

housing: Whether the client has a mortgage. Binary. "yes", "no"

loan: Whether the client has a personal loan. Binary. "yes", "no"

contact: Type of last contact communication. Categorical. One of "unknown", "telephone", "cellular"

duration: Duration of the last contact, in seconds. Numeric.

campaign: Number of contacts performed during the campaign. Numeric. y: Whether the client has subscribed to a term deposit. Binary. "yes", "no"