Explore 7,043 customer records from IBM's public Telco sample. Every tile is computed in your browser. Select a bar to filter the view.
Interactive recreation of the private system, replaying recorded fixtures.
Data files: raw CSV (7,043 rows) · rows.json · provenance.json · model.json · IBM source · full analysis ↗
Tenure is treated as time to event. Active customers are censored at their current tenure because this dataset is a snapshot. Curves respect every filter except contract itself.
Expected net equals save chance × Σ pᵢ × horizon × chargesᵢ for targeted customers, minus offer cost × targeted customers. The validation workbench shows held-out reliability. Move each assumption to test sensitivity.
Loading held-out evaluation provenance…
The dot marks a unique optimum when one exists.
Evaluation cohort and split provenance load from provenance.json.
IBM sample customer IDs; recorded churners are excluded. The table previews 50 rows; export includes the full filtered queue.
Diagonal = perfect calibration. Points are equal-width probability bins on untouched predictions.
Bars are each input's contribution to the log-odds: gold pushes toward churn, blue pulls away. This is the model's own arithmetic, not a post-hoc explainer.
Required: the 18 model inputs from the IBM Telco CSV. customerID and non-model columns may be included.
The logistic regression coefficients ship in model.json and run in the browser. The model was trained on the public IBM Telco dataset with a 25% stratified hold-out. TotalCharges was omitted because it overlaps with tenure multiplied by monthly charges.