visit the GitHub Repository.

Telecom Churn Prediction Project

In the competitive telecom industry, understanding and predicting customer churn is crucial for maintaining profitability. This project focuses on analyzing customer-level data from a leading telecom firm to build predictive models for identifying high-risk churn customers, particularly in the high-value segment. The goal is to provide actionable insights and strategies to reduce customer churn.

Table of Contents

• Project Overview
• Dataset Description
• Churn Definition
• High-Value Churn
• Project Goals
• Contents
• Usage
• Conclusion
• Repository Link
• Authors

Project Overview

The telecom industry faces a significant challenge with customer churn. Acquiring new customers is often more expensive than retaining existing ones. This project aims to analyze customer data to identify key indicators of churn and develop predictive models to target retention efforts effectively.

Dataset Description

The dataset includes customer-level information focusing on usage, revenue, and other relevant factors in the Indian and Southeast Asian telecom markets.

Churn Definition

Churn is identified as customers who have not used any services (like outgoing calls, internet, SMS, etc.) for a certain period, which is particularly relevant for prepaid customers.

High-Value Churn

Focusing on the top 20% of customers, who contribute to approximately 80% of the telecom industry’s revenue, this project aims to reduce churn in this high-value customer segment.

Project Goals

1. Analyze customer behavior and patterns in the telecom dataset.
2. Identify key indicators of churn among high-value customers.
3. Develop predictive models for accurate churn prediction.
4. Evaluate model performance and select the most effective one.
5. Provide business insights and recommendations based on the analysis.

Contents

• telecom_churn_data.csv: Dataset with customer-level data.
• telecom_churn.ipynb: Jupyter Notebook for data preprocessing, feature engineering, model building, and evaluation.
• README.md: Overview of the project.

Usage

1. Download telecom_churn_data.csv and place it in the same directory as the Jupyter Notebooks.
2. Open telecom_churn.ipynb and execute the cells for data processing and model evaluation.
3. Review the results, insights, and recommendations.

Conclusion

This project assists telecom companies in predicting and reducing churn among high-value customers, thereby minimizing revenue loss and enhancing customer retention.

Repository Link

For a detailed analysis, code implementation, and model evaluation, visit the GitHub Repository.

Authors

• Kunal
• Co-Author: Yogesh Kumar Pati