Customer Churn Prediction

About The Project

Customer churn is considered as one of the most central problems for companies. Because of the direct influence on the revenues, particularly in high investment telecommunication companies. The companies are actively pursuing for developing methods to predict customers to churn. In addition, churn prediction modeling is exceptionally essential for companies to mitigate customers in order to reduce costs.

Overview

This project presents a comparative study of eight machine learning (ML) techniques to predict customer churn. They are ridge classifier, gradient booster, adaptive boosting (AdaBoost), bagging classifier, k-nearest neighbor (kNN), decision tree, logistic regression, and random forest. We applied those techniques to a dataset from a leading public telecommunication company in Indonesia. Finally, as a means of comparison, we tested a series of model evaluation metrics.

Project Files Description

• The Battle of Neighborhoods-Starting a Cloud Kitchen Business.ipynb - Main script file.
• Inner Jakarta Cities District Coordinates.csv - Data of inner Jakarta districts coordinates to help for visualization.
• Report-The Battle of Neighborhoods - Cloud Kitchen Jakarta.docx - Written report that explains this project in detail.

More Information

Link to Medium: https://rezabosnia.medium.com/the-battle-of-neighborhoods-starting-a-cloud-kitchen-business-2342e15ee93c