Customer churn or customer attrition is a tendency of clients or customers to abandon a brand and stop being a paying client of a particular business or organization. The percentage of customers that discontinue using a company’s services or products during a specific period is called a customer churn rate. Several bad experiences (or just one) are enough, and a customer may quit. And if a large chunk of unsatisfied customers churn at a time interval, both material losses and damage to reputation would be enormous.
A reputed bank “ABC BANK” wants to predict the Churn rate. Create a model by using different machine learning approaches that can predict the best result.
This is a public dataset, The dataset format is given below.
Inside the dataset, there are 10000 rows and 14 different columns.
The target column here is Exited here.
The details about all the columns are given in the following data dictionary -
| Variable | Definition |
|---|---|
| RowNumber | Unique Row Number |
| CustomerId | Unique Customer Id |
| Surname | Surname of a customer |
| CreditScore | Credit Score of each Customer |
| Geography | Geographical Location of Customers |
| City_Category | Category of the City (A,B,C) |
| Gender | Sex of Customers |
| Age | Age of Each Customer |
| Tenure | Number of years |
| Balance | Current Balance of Customers |
| NumOfProducts | Number of Products |
| HasCrCard | If a customer has a credit card or not |
| IsActiveMember | If a customer is active or not |
| EstimatedSalary | Estimated Salary of each Customer |
| Exited | Customer left the bank or Not (Target Variable) |
In order to create a model these are the following procedure -
- Split the dataset in 70% of Train set and 30% of Test Set
- Feature engineering
- Check the accuracy score for both Training and Test Set
- Compare the accuracies for both Training and Test set, in order to check for the overfitting issues