5-Model Soft-Voting Ensemble
Engine Online & Ready

Customer Purchase Likelihood

Predicts retail customer purchase conversion by simulating five state-of-the-art gradient boosting classifiers: AdaBoost, Gradient Boosting, XGBoost, LightGBM, and CatBoost.

Quick Test Profiles:

Customer Features

8 Inputs
32 yrs
$65,000
4 orders
Electronics (0)
14.5 mins
2 deals

Prediction Output

Live Soft-Voting
Will the customer purchase?
✓ Yes
Ensemble Confidence 85.4%

Ensemble Sub-Model Probabilities

Soft-voting weighted blend
AdaBoostClassifier 82.0%
GradientBoostingClassifier 86.5%
XGBClassifier 89.2%
LGBMClassifier 84.1%
CatBoostClassifier 85.0%

Key Signal Contributors

Behavioral impacts

Netlify Function JSON Response

application/json
{\n  "purchase": "Yes",\n  "confidence": 85.4\n}