WTTE-RNN a framework for churn and time to event prediction
Using an afticial neural network to predict customers who leave the bank.
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
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.
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.
Streamlit based web application for churn prediction
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
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.
Determining the churn rate of a bank and predicting which of their customers are at high risk of leaving the bank.
This project involves performing customer segmentation and RFM (Recency, Frequency, Monetary) analysis on customer data from a retail company. The primary goal is to categorize customers into segments based on their buying behavior and identify potential target groups for marketing campaigns.
It is focused on customer churn utilizing the personal info and various user statistics on a telecom user database from U.S. The performance of the final model is 98.8% accuracy and 96.4% f-beta score so far.
In this project, you will implement your learnings to identify credit card customers that are most likely to churn. The completed project will include a Python package for a machine learning project that follows coding (PEP8) and engineering best practices for implementing software (modular, documented, and tested). The package will also have th…
The effect of social interaction on individual churn decision in MMORPG Game
This project aims to perform customer segmentation and revenue prediction for a gaming company based on customer attributes. The company wants to create persona-based customer definitions and segment customers based on these personas to estimate how much potential customers can generate in revenue.
"ChurnMaster is an advanced machine learning tool designed to predict customer churn by analyzing behavioral patterns and usage data to help businesses enhance customer retention strategies.
A dynamic risk assessment system in which a customer churn model is monitored after deployment.
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