Bank Customer Churn Prediction

Churn : Percentage of Customers that stop using product or services provided by company.

Problem Statement:

According to the result/research the cost of acquiring new customers is more than the cost of keeping existing ones. For this reason firms prefer to retain existing customers than add new ones and apply policies in this direction. Problem is how to retain existing customers?(how to decrease or prevent customemr churn?)

Solution:

Analyze the bank customer behavior using the bank (firm) customer behavior data and estimate customer churn based on these behavior which allow banks to develop new customer strategies to retain existing customers or reduce customer churn.

Objective:

Build a machine learning model that will predict customer churn (whether customer will stay or not) based on customer behavior data.

Requirements:

To run this project, you will need to download the following :

IDE: jupyternotebook

python version : 3.9.12

libraries : mentioned in requirements.txt

Task:

• Import Required library
• Importing Dataset
• Reading Dataset
• Describing Dataset
• Cleaning Dataset
• Separating dependant and independant varaible
• Feature Tansformation
• Feature Scaling
• Splitting Dataset into Train and Test
• Model Building |Logistic Regression|
• Model Evaluation

Conclusion :

Sucessfully built a Bank Customer Churn Prediction model that predicts the churn rate of a Customer with an accuracy of 77%.