Zain The AnalystAvailableCase Study
Machine Learning and AI
Telco Customer Churn Prediction
A Python machine learning project that predicts telecom customer churn and supports retention decisions.
Key Metrics Analyzed
Problem TypeClassification
IndustryTelecom
Business Use CaseCustomer Churn
Repository TypeGitHub
Overview & Objectives
This GitHub project predicts telecom customer churn using machine learning. It is relevant for the portfolio because churn prediction is a strong business analytics use case for customer retention.
Project Goal
Build a churn prediction model that helps identify telecom customers at risk of leaving.
The Business Problem
Telecom companies need to understand which customers are likely to leave. The challenge was to prepare customer account data and build a classification model that helps identify churn risk.
Methodology
My Approach
- 1Cleaned and prepared telecom customer data.
- 2Processed contract, billing, and service features.
- 3Trained classification models for churn prediction.
- 4Evaluated model performance and churn risk indicators.
Implementation
Roadmap Execution
Data Cleaning
Cleaned telecom customer records.
Feature Preparation
Prepared contract, billing, and service features.
Model Training
Trained machine learning models to predict churn.
Evaluation
Measured model performance using classification metrics.
Retention Insight
Connected model output to retention decision-making.
Key Features
Churn prediction
Customer retention analysis
Feature preparation
Model training
Model evaluation
Business Impact
Supports customer retention planning
Shows practical ML classification skills
Identifies high-risk customers
Useful for telecom business analytics
Challenges Overcome
- Preparing customer account data
- Handling mixed categorical and numerical features
- Evaluating churn classification performance
- Making churn insights understandable
Outcomes
Final Outcomes & Learnings
- Created a churn prediction workflow for telecom customer retention.
- The project demonstrates applied machine learning, classification, and customer analytics.
Project Gallery & Screenshots
Technologies Used
PythonPandasScikit-learnMachine LearningClassificationCustomer Analytics
Data Sources
Telco customer datasetContract dataBilling dataService usage dataChurn labels
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