Study existing demographic data and predict the customers who will accept the offer of a credit card. The study is focused on 18.000 customers.
- Objective
- Role
- Dataset
- Tools
- Models and Code
- Results
Build a model that will provide insight into why some bank customers accept credit card offers.
Bank's Risk Analyst.
The bank wnats to understand the demographics and other characteristics of its customers who accept and do not a credit card offer.
The marketing study is focused on 18000 current bank customers.
The original dataset as well as the transformed or cleaned set are available in the dataset folder.
Definitions of the original features:
- Customer Number: A sequential number assigned to the customers (this column is hidden and excluded – this unique identifier will not be used directly).
- Offer Accepted: Did the customer accept (Yes) or reject (No) the offer. Reward: The type of reward program offered for the card.
- Mailer Type: Letter or postcard.
- Income Level: Low, Medium, or High.
- Bank Accounts Open: How many non-credit-card accounts are held by the customer.
- Overdraft Protection: Does the customer have overdraft protection on their checking account(s) (Yes or No).
- Credit Rating: Low, Medium, or High.
- Credit Cards Held: The number of credit cards held at the bank.
- Homes Owned: The number of homes owned by the customer.
- Household Size: The number of individuals in the family.
- Own Your Home: Does the customer own their home? (Yes or No).
- Average Balance: Average account balance (across all accounts over time). Q1, Q2, Q3, and Q4
- Balance: The average balance for each quarter in the last year
- Jupyter Notebook 6.4.5
- Python 3.9
- Tableau 2022.2.1
- MySQL Workbench 8.0
The final notebook is contained in the code folder.
In the presentation in Tableau a summary of the models is explained.
There is not a backup of the models.
The demographics of the customers as well as insights to improve the performance of the marketing campaigns are explained in the presentation in Tableau.