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

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

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.

Using an afticial neural network to predict customers who leave the bank.

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.

毕业论文实验代码

Streamlit based web application for churn prediction

Churn Prediction using PySpark

A churn prediction app built with Gradio and wrapped around a machine learning model

Customers knowledge, supply chain movement and sales forecasting, Customer Lifetime value, churn and survival analysis

This project is an end-to-end machine learning pipeline with a focus on efficient model deployment using Flask API, Docker, and Amazon EC2. The modular architecture ensures seamless integration and a consistent experience across environments. A CI/CD pipeline with GitHub Actions streamlines development and deployment.

A churn model is a mathematical representation of how churn impacts your business. Churn calculations are built on existing data (the number of customers who left your service during a given time period). A predictive churn model extrapolates on this data to show future potential churn rates.

Predicting churn in a real company for CRM actions.

Customer churn train/prediction library with automatic dataset size optimisation features.

This repository contains 2 ML projects for my internship under NeuroNexus Innovations.

This project was the outcome of the Corporate Reseach Project as a part of Masters in Data Sciences and Business Analytics program at ESSEC-CentraleSupelec and monitored by Deloitte. In this study, we propose and evaluate a predictive model for employee churn using machine learning techniques.

Predicting Customer Churn in a Bank using ANNs

AI + Machine Learning + Deep Learning + Neural Networks = Self-Learning Adventure

Developed a model to predict customer churn for subscription-based businesses.

Churn Modelling - unusual rate at which customers leaving the company, we need to figure out why? we need to understand the problem? We actually need to create a demographic segmentation model to tell the bank/company which customers are at high risk of leaving.