Mihirrish/ShopSphere-AICV-TabTransformer-v2

ShopSphere AICV TabTransformer (v2)

Custom TabTransformer-style PyTorch model for churn prediction, trained on a synthetic ShopSphere e-commerce dataset for an academic Data-Driven Marketing Analytics project.

Dataset

Synthetic academic dataset: 12,000 observations, 1,000 customers, 12 months each, covering Historical / Update / Post-Update periods.

Baseline comparison

Model	Accuracy	Precision	Recall	F1	ROC-AUC	Threshold
Logistic Regression	0.5094	0.1403	0.8176	0.2394	0.7347	0.35
Gradient Boosting	0.1450	0.0985	0.9882	0.1792	0.6824	0.02
TabTransformer	0.4456	0.1290	0.8471	0.2240	0.7325	0.17

Decision threshold

Selected on the validation set by maximizing expected net value under the assumption that a missed churner costs 25x more than an unnecessary retention outreach (₹5,000 lost value vs. ₹200 intervention cost) — not by F1.

Limitations

The dataset is synthetic and does not represent real ShopSphere customers. Results, including the baseline comparison, should not be interpreted as causal evidence that the platform algorithm update caused churn, and the hyperparameter search covered a small, compute-budget-limited grid rather than an exhaustive search.

Intended use

Academic demonstration of predictive marketing analytics and model hosting.