{"repo":"AmirhosseinHonardoust/Subscription-Loyalty-Risk-Radar","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Subscription-Loyalty-Risk-Radar","clone":"git clone https://github.com/AmirhosseinHonardoust/Subscription-Loyalty-Risk-Radar.git","description":"A customer intelligence engine that predicts subscription probability, models purchase frequency, and computes a unified loyalty risk score. Includes explainability, segment insights, and a scenario simulator, all integrated into an interactive Streamlit dashboard.","language":"Python","stars":15,"topics":["business-intelligence","churn-prediction","customer-loyalty","customer-segmentation","data-science","explainable-ai","feature-engineering","machine-learning","marketing-analytics","predictive-modeling"],"license":"MIT","category":"machine-learning","readme_excerpt":"Subscription Loyalty Risk Radar An intelligence engine that transforms raw shopping behavior into subscription insights, frequency predictions, loyalty scoring, and scenario simulation, designed for teams that want to understand not only what customers do, but why they behave the way they do. --- Overview Retail loyalty is not a single action, it is a behavioral signature that emerges from repeated decisions: purchasing rhythms, shipping preferences, discount sensitivity, past experiences, and long-term commitment tendencies. Yet most companies reduce loyalty to naive metrics like “number of purchases” or “subscription status.” This leads to simplistic marketing decisions and predictable churn. Subscription-Loyalty-Risk-Radar takes a more scientific view: Loyalty is multi-dimensional Behavior must be quantified Predictions must be explainable Insights must be actionable This project builds a full-stack ML system that: 1. Predicts subscription probability Who is likely to subscribe? Who is unlikely? Why? 2. Models purchase frequency How often will a customer buy? What is their behavioral “intensity score”? 3. Creates a unified Loyalty Risk Score (0–100) A single interpretable metric combining short-term behavior + long-term intent. 4. Provides explainability for each score Which features raised or lowered loyalty? What factors shape behavior? 5. Simulates what-if scenarios What happens if you offer a discount? Change shipping speed? Add a promo? 6. Visualizes everything in an ","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Subscription-Loyalty-Risk-Radar/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}