survival analysis

Model Perspective

Nov 3, 2023 · Fundamentals

Why Exponential & Weibull Distributions Matter: Key Concepts and Applications

This article introduces the exponential and Weibull distributions, explains their probability density and cumulative functions, highlights key properties such as the memoryless nature of the exponential and the flexibility of Weibull, and demonstrates practical calculations for reliability and survival analysis scenarios.

exponential distributionprobabilityreliability

0 likes · 6 min read

Why Exponential & Weibull Distributions Matter: Key Concepts and Applications

DataFunTalk

Jan 2, 2022 · Fundamentals

Survival Analysis for User Churn: Concepts, Data Preparation, and Quantitative Modeling

This article introduces survival analysis, explains how to model user churn by defining purchase and cancellation times as birth and death events, describes data formatting, presents descriptive Kaplan‑Meier results, and shows how Cox regression quantifies the impact of factors such as membership and activity on user survival.

Cox regressiondata analysisstatistical modeling

0 likes · 7 min read

Survival Analysis for User Churn: Concepts, Data Preparation, and Quantitative Modeling

DataFunTalk

Dec 22, 2021 · Artificial Intelligence

Applying Survival Analysis to User Activity Modeling: Concepts, Methods, and the KwaiSurvival Deep‑Learning Framework

This article explains why traditional DAU metrics are insufficient, introduces survival analysis fundamentals and key functions, demonstrates how Kaplan‑Meier curves can characterize user activity, and presents KwaiSurvival—a deep‑learning‑based survival modeling suite with DeepSurv, DeepHit and N‑MTLR models—for practical user‑engagement and churn‑prevention use cases.

Cox regressionKM curveKwaiSurvival

0 likes · 15 min read

Applying Survival Analysis to User Activity Modeling: Concepts, Methods, and the KwaiSurvival Deep‑Learning Framework

DataFunTalk

Oct 15, 2021 · Artificial Intelligence

Risk Control and Operations for Existing Credit Customers: Models, Strategies, and Practices

This article examines how financial institutions can manage risk and improve operations for existing loan customers by analyzing client flow, regulatory impacts, accelerated deterioration, and layered segmentation, and by applying advanced models such as rule‑based alerts, B‑card scoring, LSTM, and survival analysis to enable timely risk detection and targeted cross‑selling.

customer segmentationfinancial modelingmachine learning

0 likes · 20 min read

Risk Control and Operations for Existing Credit Customers: Models, Strategies, and Practices

DataFunSummit

Oct 15, 2021 · Artificial Intelligence

Risk Control and Operations for Existing Loan Customers

This article examines how financial institutions can manage risk and improve operations for existing loan customers by analyzing customer flow during the pandemic, policy impacts, rapid deterioration patterns, and by applying advanced models such as LSTM, survival analysis, and B‑card strategies to enable timely risk detection and targeted cross‑selling.

LSTMbehavior modelingcustomer operations

0 likes · 19 min read

Risk Control and Operations for Existing Loan Customers