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📊 Telco Customer Churn Prediction Machine Learning-Based Retention Strategy Optimization 📖 Project Overview
Customer churn is one of the biggest challenges in subscription-based industries like telecommunications. Acquiring new customers is significantly more expensive than retaining existing ones.
This project builds a machine learning model to predict customer churn using demographic, service usage, and billing data. The objective is to identify high-risk customers early and enable proactive retention strategies.
🎯 Business Problem
Telecom companies face:
High market competition
Low switching barriers
Rising customer acquisition costs
The inability to identify customers likely to churn leads to revenue loss and inefficient retention campaigns.
Objective: Develop a predictive system to identify customers at risk of churning before they leave.
📊 Dataset Information
Total Customers: 7,043
Total Features: 20 (excluding target variable)
Target Variable: Churn (Yes/No)
Feature Categories:
Demographics
Service Usage
Contract Details
Billing Information
🔎 Exploratory Data Analysis (EDA)
Key insights discovered:
Month-to-month contracts show highest churn rates
Customers with low tenure churn more frequently
Higher monthly charges correlate with increased churn
Electronic check payment users exhibit higher churn behavior
⚙️ Data Preparation & Feature Engineering
Missing value handling
Categorical variable encoding
Feature scaling
Stratified 80/20 train-test split
Creation of tenure groups
Engineering billing and contract indicators
These steps improved model interpretability and predictive performance.
🤖 Modeling Approach Model Used:
Logistic Regression (Primary Model)
Evaluation Metrics:
Accuracy
Precision
Recall
F1-Score
Special focus was placed on Recall for churn (Class 1) to minimize missed churn cases.
📈 Model Performance
Overall Accuracy: 79%
Recall (Non-Churn): 94%
Recall (Churn): 43%
Precision (Churn): 72%
Key Insight:
The model performs well in identifying stable customers but requires improvement in detecting churn cases (false negatives).
💡 Business Impact
Establishes a baseline predictive retention system
Identifies high-risk customer segments
Supports targeted retention campaigns
Enables data-driven decision making
False negatives (missed churn cases) highlight opportunity for further model optimization.
🚀 Strategic Recommendations
Improve churn recall using class balancing and advanced models
Focus retention efforts on high-risk segments:
Month-to-month contracts
Low tenure customers
High monthly charges
Electronic check users
Implement risk-based retention strategy instead of blanket campaign
Conclusion
This project demonstrates how predictive analytics can shift churn management from reactive response to proactive revenue protection strategy.
Author
Subodh Kumar Machine Learning & Data Analytics Enthusiast