CHURN-DATA VISUALISATION

Learning project for Data Science course at SkillFactory

The main objective of this project is to practice visualisation methods and use the acquired charts for an overall understanding of the data trends.

About the dataset:

Customer data from a bank to construct a predictive model for the likely churn clients.

Source

• CustomerId: Customer identifier
• Surname: Customer's surname
• CreditScore: Customer's credit score (higher scores indicate more loans taken and repaid)
• Geography: Customer's country (bank is international)
• Gender: Customer's gender
• Age: Customer's age
• Tenure: Number of years the customer has been with the bank
• Balance: Customer's balance in the bank
• NumOfProducts: Number of bank services the customer has acquired
• HasCrCard: Whether the customer has a credit card (1 for yes, 0 for no)
• IsActiveMember: Whether the customer is an active bank member (1 for yes, 0 for no)
• EstimatedSalary: Customer's estimated salary
• Exited: Loyalty status (1 for churned customer, 0 for loyal customer)

Implemented visualisation libraries

• plotly