THE RESULT
What the data shows
The largest computed metric is 0.3645 for Random forest; the smallest is 0.1495 for Prior baseline. Metric: average_precision (Average precision).
This is completed-session classification, not real-time conversion forecasting. Exact input duplicates are grouped.
| model | accuracy | balanced accuracy | macro f1 | roc auc | average precision | brier |
|---|---|---|---|---|---|---|
| Prior baseline | 0.8505 | 0.5 | 0.4596 | 0.5 | 0.1495 | 0.1272 |
| Logistic regression | 0.8493 | 0.5038 | 0.4698 | 0.7342 | 0.2881 | 0.1171 |
| Random forest | 0.8505 | 0.5225 | 0.5084 | 0.7748 | 0.3645 | 0.1109 |
THE METHOD
From source to answer
Fixed grouped-input train/test benchmark with a prior baseline.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
EVALUATION
How the result was checked
Fixed 80/20 split of unique input groups (seed 42); exact input duplicates stay together.
Fixed models; default decision threshold; no holdout tuning.
Target and input features
Target: revenue
Inputs: administrative, administrative_duration, informational, informational_duration, productrelated, productrelated_duration, bouncerates, exitrates, specialday, month, operatingsystems, browser, region, traffictype, visitortype, weekend
THE NEXT DECISION
What follows from the finding
Use the benchmark to study session behavior; obtain time-stamped partial-session data for any earlier intervention.
Where the conclusion stops
One observation is a completed session. End-of-session behavior cannot support an early-session prediction claim. PageValues is excluded from purchase models because it can encode downstream purchase information. Associations are not experiment results.
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.