To predict likelihood of a liability customer buying personal loans, using some Machine Learning algorithms.

Data Description: The file Bank_Personal_Loan_Modelling.xlsx 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.

Context: The bank has a growing customer base. The bank wants to increase borrowers (asset customers) base to bring in more loan business and earn more through the interest on loans. So , the bank wants to convert the liability based 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. The department wants you to build a model that will help them identify the potential customers who have a higher probability of purchasing the loan. This will increase the success ratio while at the same time reduce the cost of the campaign.

Attribute Information: ● 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 code. ● 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?