The dataset contains data on customers. The data include customer demographic information , the customer's relationship with the shop and the customer response to the last campaign. Run models to answer various questions
The dataset contains data on 5000 customers. The data include customer demographic information (age, income, etc.), the customer's relationship with the bank (mortgage, securities account, etc.), and the customer response to the last personal loan campaign (Personal Loan). Among these 5000 customers, only 480 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign.
This case is about a bank (Thera Bank) whose management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio with a minimal budget.
ID: Customer ID
Age: Customer's age in completed years
Experience: #years of professional experience
Income: Annual income of the customer ($000)
ZIP Code: Home Address ZIP
Family: Family size of the customer
CCAvg: Avg. spending on credit cards per month ($000)
Education: Education Level. 1: Undergrad; 2: Graduate; 3: Advanced/Professional
Mortgage: Value of house mortgage if any. ($000)
Personal Loan: Did this customer accept the personal loan offered in the last campaign?
Securities Account: Does the customer have a securities account with the bank?
CD Account: Does the customer have a certificate of deposit (CD) account with the bank?
Online: Does the customer use internet banking facilities?
Credit card: Does the customer use a credit card issued by the bank?
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Hello, I am Python and Machine Learning expert. I see these are instructions from a Kaggle competition. I guess you are looking for some unsupervised learning on your customer data? Best Regards, Borut
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