In the telecommunication industry, customers tend to change operators if not provided with attractive schemes and offers. It is very important for any telecom operator to prevent the present customers from churning to other operators.

For many incumbent operators, retaining high profitable customers is the number one business goal.

To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.

As a data scientist, your task in this case study would be to build an ML model which can predict if the customer will churn or not in a particular month based on the past data.

• General Info
• Technologies Used
• Conclusions
• Acknowledgements

In the telecommunication industry, customers tend to change operators if not provided with attractive schemes and offers. It is very important for any telecom operator to prevent the present customers from churning to other operators.

Using random forest with PCA, we got good result with arpounf 90% accuracy on telecom churn prediction other than the other model such as logistic regression.

But Logistic gression with RFE and VIF helped us to identify the important features to look into to predict the Churn

• Python - 3.9.12
• numpy - 1.21.5
• pandas - 1.4.2
• matplotlib
• seaborn - 0.11.2
• sklearn

• This project was inspired by upGrade AI & ML course case study

Created by @sandipanp - feel free to contact me!