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Machine Learning Beginner

ChurnShield

Telecom customer churn prediction

Predict which customers are likely to leave, explain why, and turn it into a retention list the business can act on.

SHIPSTACK · PROJECT ChurnShield Telecom customer churnprediction Beginner Machine Learning 1 Customer data 2 Features 3 Models 4 Retention app 5 milestones

The scenario

ChurnShield is the classic ML project done properly: exploratory analysis, feature engineering, model comparison, threshold tuning for business cost, SHAP explanations, and a small app that scores customers and explains each score.

System architecture

01 Customer data Usage + billing
02 Features Engineered
03 Models Compared + tuned
04 Retention app Scores + reasons

High-level flow of ChurnShield, from input to output.

What you will learn

  • End-to-end ML workflow
  • Choosing thresholds by business cost
  • Model explanations with SHAP

What you will build

  • EDA notebook
  • Training pipeline
  • Scoring and explanation app

Build plan

  1. EDA
  2. Features
  3. Models
  4. Explain
  5. App

Tech stack

pandasscikit-learnXGBoostSHAPStreamlit

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