WTTE-RNN a framework for churn and time to event prediction

Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.

Unlock actionable insights and boost customer retention with this Power BI project. Analyze and visualize risk factors to proactively prevent churn. ➡️

零售电商客户流失模型，基于tensorflow，xgboost4j-spark，spark-ml实现LR，FM，GBDT，RF，进行模型效果对比，离线/在线部署方式总结

Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)

A Python package for survival analysis. The most flexible survival analysis package available. SurPyval can work with arbitrary combinations of observed, censored, and truncated data. SurPyval can also fit distributions with 'offsets' with ease, for example the three parameter Weibull distribution.

An End to End Customer Churn Prediction solution using AWS services.

Typescript library to access Faraday's API infrastructure for B2C predictions

Developed a churn prediction model using XGBoost, with comprehensive data preprocessing and hyperparameter tuning. Applied SHAP for feature importance analysis, leading to actionable business insights for targeted customer retention.

Welcome to some case study of data science projects - (Personal Projects).

This repository will have all the necessary files for machine learning and deep learning based Banking Churn Prediction ANN model which will analyze tha probablity for a customer to leave the bank services in near future. Deployed on Heroku.

Build an scikit-learn model to predict churn using customer telco data.

This repository consists of predicting dynamic pricing, churn predictions using sales and marketing data for understanding users' behaviour.

Predict customer churn with text and interpretability.

Telecommunication Churn Price Prediction Project. Full Project with Code, Documents and Research papers

Real-time behavioral intelligence for call centers. Transcribes support calls, redacts PII, extracts emotional tone, classifies issues, and delivers insight-rich dashboards — powered by GPT-3.5 (cheap tokens), Whisper, DuckDB, and a polished React+TypeScript frontend. No Azure. No Power BI. No vendor lock-in. Just full-stack AI that runs local.

毕业论文实验代码

Analysis and Prediction of the Customer Churn Using Machine Learning Models (Highest Accuracy) and Plotly Library

A production-grade MLOps pipeline for predicting telecom customer churn, featuring automated data preprocessing, ML model training, experiment tracking with MLflow, distributed training using PySpark, real-time inference via Kafka streaming, Airflow DAG orchestration, and Dockerized REST API deployment.