Telecom customer's churn using machine learning

Summary

Motivation: As part of my data science bootcamps projects, we intent to perform "customer churn modeling" for company within the telecom industry

Goal: The goal is to test and train multiple machine learning models in order to select the one with most valid business case

Business Case: The company will be using this model to predict customers at risk of churning. hypothetical case was:
1- The contract's life time value is 1000$
2- Customers at risk of churning will be called and offered 10% discount if they maintain/continue their contract
3- based on the model results, what is the net effect on the company's income statement

Next Steps:
- Test if customer's location has any impact on customer's behaviour
- Test if manipulating correlated features will have impact on customer's behaviour and subsequently churning rate
- Expand more on hyperparameter tuning
- Expand more on feature importance and selection

Introduction

The approach was as following

Models tested

The following Machine learning models were tested:

- Logistic Regression
- SVM
- KNN
- RandomForest
- AdaBoost
- Gradient Boost
- Decision Tree
- XGBoost
- PyCaret classification module

Model chosen:

Logistic Regression with the following results:

Business Case clarification: