Let's build a no-code tool for small businesses to reduce churn
summary
A runnable, no-code churn tool is built in a day using pandas, the lifelines library with a Cox proportional hazards model and a Streamlit frontend, then pushed to Heroku. It predicts per-customer survival curves conditioned on tenure (example: known tenure 22 months, qth survival shows 50% risk at ~30 months — ~8 months to act), shows feature hazard ratios (total charges, payment method, contract length), and notes the model is weak for very long tenures but effective for early churn intervention.
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chapters (11)
- 0:00Start
- 3:53Outline
- 7:20The goal is to identify churn behavior proactively
- 8:28We will build the demo using Telecom Churn data available on Kaggle
- 11:14Churn Analysis Models
- 12:25Build a model - Survival Analysis using lifelines package - Predicts the survival chances at any given point of time
- 19:47Feature importance plot
- 22:18Web Application is built using streamlit
- 23:40The entire app is deployed on Heroku
- 24:25Application Architecture
- 29:07We build data-driven products to help business stakeholders
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