PREDICTIVE DATA • TIME SERIES

E-Commerce Churn & Demand Forecast

Time-series forecasting model combining XGBoost and PyTorch LSTM to predict customer re-order intervals with 94.2% precision.

Problem Statement

An e-commerce brand with 500,000+ active customers struggled with overstocking warehouse inventory while losing high-value subscribers to silent churn.

Solution & Architecture

Rohit engineered a **Hybrid XGBoost + PyTorch LSTM Predictive Pipeline**:

  • PyTorch LSTM: Analyzed sequential purchase interval history across 8M transaction logs.
  • XGBoost Churn Risk Scoring: Identified churn risk triggers 14 days prior to subscription lapsing.

Verified Customer Testimonial

"Rohit's demand forecasting model achieved 94.2% accuracy on customer re-order intervals. We reduced our warehouse overstock holding costs by 32% while recovering thousands of at-risk subscribers. Superb data science work!"

Hiroshi Tanaka

Quantifiable Results

94.2%
Forecast Precision
32%
Overstock Reduction
+18%
Retention Lift

Project Specs

Category: Predictive Data Science
Status: Active Production
Architect: Rohit
Tech Stack Used
XGBoost PyTorch Pandas

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