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.
WORK FLOW :
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.
- Save the model for futher prediction
OUTPUT:
From the review of the fitted models, the best model that gives a decent balance of the recall and precision is the random forest where according to the fit on the training set, with a precision score of 0.86, out of all customers that the model thinks will churn, 86% do actually churn and with the recall score of 0.86.