Supervised Learning Classification Decision-Tree

• Project completed as part of Great Learning's Postgraduate Program - - Artificial Intelligence & Machine Learning
• Project delivered in January 2024
• Repository includes two files:
  • Jupyter notebook with Python code
  • CSV file includes data imported into notebook

Problem Statement : Personal Loan Campaign

To predict whether a liability customer will buy personal loans, to understand which customer attributes are most significant in driving purchases, and to identify which segment of customers to target more.

Data Dictionary

• ID: Customer ID
• Age: Customer’s age in completed years
• Experience: #years of professional experience
• Income: Annual income of the customer (in thousand dollars)
• ZIP Code: Home Address ZIP code.
• Family: the Family size of the customer
• CCAvg: Average spending on credit cards per month (in thousand dollars)
• Education: Education Level. 1: Undergrad; 2: Graduate;3: Advanced/Professional
• Mortgage: Value of house mortgage if any. (in thousand dollars)
• Personal_Loan: Did this customer accept the personal loan offered in the last campaign?
• Securities_Account: Does the customer have securities account with the bank?
• CD_Account: Does the customer have a certificate of deposit (CD) account with the bank?
• Online: Do customers use internet banking facilities?
• CreditCard: Does the customer use a credit card issued by any other Bank (excluding All life Bank)?