Mihir Bhavigadda

Custom TabTransformer-style PyTorch model for churn prediction, trained on a synthetic ShopSphere e-commerce dataset for an academic Data-Driven Marketing Analytics project. covering Historical / Update / Post-Update periods. 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. 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…