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Tagged articles

churn prediction

4 articles · Page 1 of 1
Beike Product & Technology
Beike Product & Technology
Jul 8, 2021 · Artificial Intelligence

Applying End-to-End Deep Learning Models for Real Estate Agent Churn Prediction

This article reviews the evolution of end-to-end deep learning models, describes how they were adapted and optimized for a real‑estate broker churn‑warning scenario, and presents experimental results showing significant improvements in AUC, KS and lift over traditional classifiers.

Recommendation Systemschurn predictionend-to-end models
0 likes · 17 min read
Applying End-to-End Deep Learning Models for Real Estate Agent Churn Prediction
StarRing Big Data Open Lab
StarRing Big Data Open Lab
Oct 20, 2017 · Artificial Intelligence

How to Build a Customer Churn Warning Model with R and Discover

This article demonstrates a step‑by‑step workflow for constructing a churn prediction model using R in Discover, covering data loading, preprocessing, feature extraction, labeling, random‑forest training, prediction, and evaluation to help businesses proactively retain high‑value customers.

DiscoverMachine LearningR
0 likes · 11 min read
How to Build a Customer Churn Warning Model with R and Discover
Meituan Technology Team
Meituan Technology Team
Feb 17, 2017 · Big Data

User Profiling and Machine Learning Practices for Food Delivery O2O Platforms

Meituan Delivery’s rapid expansion across multiple categories relies on detailed user profiling and machine‑learning models—such as high‑potential customer prediction, churn risk regression and Cox survival analysis—to personalize acquisition, retention, and scenario‑based cross‑selling, while addressing sparse behavior, unstructured data, and geographic context challenges.

Big DataMachine LearningO2O
0 likes · 13 min read
User Profiling and Machine Learning Practices for Food Delivery O2O Platforms
dbaplus Community
dbaplus Community
Dec 25, 2015 · Artificial Intelligence

Detecting Fraudulent ModemPOOL Terminals with K‑Means Clustering

This article details how telecom operators can identify fraudulent ModemPOOL (cat‑pool) terminals and predict churn using data‑driven clustering and day‑interval warning models, covering metric selection, data exploration, k‑means clustering, model deployment, and performance evaluation.

ClusteringK-MeansModel Deployment
0 likes · 18 min read
Detecting Fraudulent ModemPOOL Terminals with K‑Means Clustering