Bank Data Description For Bankdata File From DePau

Question

Bank Data Description (For bankdata file obtained from DePaul Universit The dataset contains attributes on each person’s demographics and banking information in order to determine they will want to obtain the new PEP (Personal Equity Plan). Your goal is to perform Association Rule discovery on the dataset using R. First, perform the necessary preprocessing steps required for association rule mining, specifically the id field needs to be removed and a number of numeric fields need discretization or otherwise converted to nominal. Next, set PEP as the right hand side of the rules, and see what rules are generated. Select the top 5 most “interesting” rules and for each specify the following: support, confidence, and lift values; an explanation of the pattern and why you believe it is interesting based on the business objectives of the company; and any recommendations based on the discovered rule that might help the company better understand customer behavior or develop a business opportunity.

Dr. Jack HW Helper · Accepted Answer

Introduction The dynamic landscape of banking and financial service industries has necessitated the use of data-driven methods to better understand customer behaviors and preferences. Association rule mining, a prominent data mining technique, enables organizations to uncover hidden patterns within large datasets, facilitating more targeted marketing and strategic decision-making. In this context, the analysis of banking customer data to identify key factors influencing the purchase of Personal Equity Plans (PEP) provides valuable insights into customer segmentation, behavior prediction, and opportunity identification. Preprocessing Steps The initial step involved cleaning and preparing the dataset (bankdata_csv_all.csv) to ensure its suitability for association rule mining. The ‘id’ attribute was eliminated as it serves merely as a unique identifier without intrinsic analytical value. Numeric attributes such as age, income, and number of children were discretized into categorical variables to convert continuous data into nominal form, which is necessary for the Apriori algorithm employed in rule discovery. Discretization was performed based on logical intervals—for example, age was segmented into ranges (e.g., 18-30, 31-45, etc.), and income was categorized into income brackets. The dataset was then transformed into a transaction-ready format by encoding each customer's attributes as itemsets, where each attribute-value pair represented an item (e.g., 'age_18-30', 'income_high', 'married_yes'). This transformation involved encoding categorical data in a format compatible with R's arules package. Parameters and Experiments Association rule mining was conducted using the Apriori algorithm with specific parameters tailored to uncover meaningful rules. The minimum support was set at 0.05, ensuring that only rules with a reasonably high occurrence were considered, while the confidence threshold was initially set to 0.6 to filter for rules with substantial predictive pow

Bank Data Description For Bankdata File From DePau

Bank Data Description (For bankdata file obtained from DePaul Universit

Sample Paper For Above instruction

Rule 1: Customers with savings accounts and owned cars are likely to buy PEP

Rule 2: Customers aged 31-45 with higher income levels are more likely to purchase PEP

Rule 3: Married customers with children and current accounts show a higher propensity for buying PEP

Rule 4: Customers from suburban regions with mortgages are less likely to buy PEP

Rule 5: Customers without current accounts but with savings accounts have higher likelihood of PEP uptake

References