Developed a predictive analytics solution to identify high-risk customers likely to churn
Access the Churn Model through streamlit web application to receive a churn risk probability score. STREAMLIT APP
In this DATASET, there are 10,000 rows, 14 columns, and the following variables:
| Variable | Description |
|---|---|
| RowNumber | Row Numbers from 1 to 10000 |
| CustomerId | Unique Ids for bank customer identification |
| Surname | Customer's last name |
| CreditScore | Credit score of the customer |
| Geography | The country from which the customer belongs |
| Gender | Male or Female |
| Age | Age of the customer |
| Tenure | Number of years for which the customer has been with the bank |
| Balance | Bank balance of the customer |
| NumOfProducts | Number of bank products the customer is utilising |
| HasCrCard | Binary Flag for whether the customer holds a credit card with the bank or not |
| IsActiveMember | Binary Flag for whether the customer is an active member with the bank or not |
| EstimatedSalary | Estimated salary of the customer in Dollars |
| Exited | Binary flag 1 if the customer closed account with bank and 0 if the customer is retained |
- Minimizing Revenue Loss from Customer Churn
- Developing a predictive analytics solution to identify high-risk customers likely to churn
- Enabling proactive retention strategies to minimize financial losses and maintain a competitive edge in the banking industry
- Unlocking Drivers of Customer Churn
- Creating a machine learning model to uncover key factors driving customer churn
- Empowering banks to address root causes
- Optimizing customer experiences, and implementing targeted retention initiatives to boost loyalty and satisfaction
Click in to the following notebook link to observe the workflow of the project. NOTEBOOK
Click in to the following link for EDA (Exploratory Data Analysis).