Dataset from a bank containing 18.000 samples from customers for a period of one year

Features in the data set are:

• 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

Build a model that can predict if a customer will accept a credit card offer or not

• Binary Classification
• Models used in this project are Logistic Regression, KNeighborsClassifier, DecisionTreeClassifier and RandomForestClassifier

• sql directory: contains slq queries connected to this project
• files directory: contains the dataset in bot xlsx and csv format
• notebooks directory: contains a jupyter notebook with the whole analysis
• tableau directory: contains the tableau workbook we created to visalize the dataset
• slides directory: contains the short presentaion slides

The main outcomes of the analysis can be viewed below or via the notebook

you can also find the dashboard here