Predicting Customer Attrition in Telecom Using Machine Learning

Project Overview

This project develops a predictive model for customer attrition in the telecom industry using advanced machine learning techniques to identify high-risk customers and enable proactive retention strategies.

Table of Contents

• Introduction
• Objectives
• Methodology
• Dataset
• Model Building
• Results and Conclusion

Introduction

The telecommunications sector is essential to the global economy, providing various services from voice calls to internet services. Customer retention is crucial due to intense competition and high acquisition costs.

Objectives

• Develop a high-accuracy predictive model for customer attrition.
• Identify key factors contributing to customer turnover.
• Formulate targeted retention strategies based on model insights.

Methodology

1. Data Collection and Preprocessing: Clean and transform customer data.
2. Feature Engineering: Identify and create influential features.
3. Model Development and Training: Train models on historical data.
4. Handling Imbalanced Data: Use SMOTE to balance the dataset.
5. Model Evaluation and Selection: Assess models using various metrics.
6. Deployment and Integration: Implement the model within the telecom operator's system.
7. Result Interpretation: Use predictions to inform retention strategies.

Dataset

The dataset includes customer demographics, service usage, billing history, and interactions, with 7,043 entries and 21 columns.

Model Building

Develop and train models such as Random Forest, SVM, Logistic Regression, and XGBoost for attrition prediction. Optimize parameters to enhance predictive accuracy.

Results and Conclusion

• With SMOTE: Improved recall, better identification of high-risk customers.
• Feature Importance: Key factors include tenure, total charges, and contract type.

• Conclusion: Random Forest combined with SMOTE provides strong predictive accuracy.

Contact

Connect with me through various portals :

	Social Media	Username	Link
	Email	[email protected]	Email
	LinkedIn	Shabazuddin Mohammad	LinkedIn
	Instagram	shabaz_uddin	Instagram
	Facebook	Shabaz	Facebook
	Twitter	shabazuddin786	Twitter

I'm always open to collaboration and new opportunities! Feel free to reach out and connect with me. 🌟

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