THE RESULT
What the data shows
The largest computed metric is 1.805 for 0.2; the smallest is 1 for 1.0. Metric: lift (Lift versus random review).
This does not estimate the causal benefit of calling; all records come from past campaigns.
| review fraction | reviewed | precision | recall | lift |
|---|---|---|---|---|
| 0.05 | 453 | 0.5342 | 0.08473 | 1.691 |
| 0.1 | 905 | 0.5326 | 0.1688 | 1.686 |
| 0.2 | 1,809 | 0.5699 | 0.361 | 1.805 |
| 0.3 | 2,713 | 0.5389 | 0.5119 | 1.706 |
| 0.5 | 4,522 | 0.4027 | 0.6376 | 1.275 |
| 1 | 9,043 | 0.3158 | 1 | 1 |
THE METHOD
From source to answer
Top-fraction lift curve using the fixed logistic model on later records.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
EVALUATION
How the result was checked
First 80% of ordered observations train; final 20% test.
Fixed models; default decision threshold; no holdout tuning.
Target and input features
Target: subscribed
Inputs: contact, month, day, campaign, pdays, previous, poutcome
THE NEXT DECISION
What follows from the finding
Choose a review capacity using operational costs and validate that choice on a fresh period.
Where the conclusion stops
Observational marketing records do not identify campaign uplift. Month and row order are not precise timestamps; repeated-client identifiers are unavailable. Models are research diagnostics, not financial eligibility or automated contact decisions.
Related studies may reuse observations or holdouts. These are historical analyses; associations and backtests do not demonstrate commercial impact. Further model tuning needs new, untouched evaluation data.