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

In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.

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.

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Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. With the help of ML classification algorithms, we are going to predict the Churn.

This is an end-to-end machine learning project that utilizes LightGBM to predict customer's probability of churning in a bank's credit card service. Identifying likely churners helps plan strategies for retention, maintaining healthy revenue. Acquiring a new customer is costlier than keeping one.

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

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

Bank customers churn dashboard with predictions from several machine learning models.

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

Churn Prediction with PySpark using MLlib and ML Packages

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

End-to-end uplift modeling pipeline for customer retention using T-Learner and X-Learner with XGBoost. Includes AUUC/Qini evaluation, SHAP interpretability, and business profit optimization.

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.

A Data science and Analytics project with the main aim of doing some Descriptive and Exploratory Data Analysis and then applying predictive modelling for predicting why and which are the best and most experienced employees leaving prematurely?

This repo contains regression and classification projects. Examples: development of predictive models for comments on social media websites; building classifiers to predict outcomes in sports competitions; churn analysis; prediction of clicks on online ads; analysis of the opioids crisis and an analysis of retail store expansion strategies using Lasso and Ridge regressions.

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

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MachineLearningSamples-ChurnPrediction

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WARNING: This repository is no longer maintained :warning: This repository will not be updated. The repository will be kept available in read-only mode.

Variational deep autoencoder to predict churn customer

This is my third project in Metis Data Science Bootcamp

This repository contains instructions and the necessary code to deploy a churn-prediction automation solution using Cortana Intelligence Suite.

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This project involves predicting customer churn in a telecommunications company using machine learning techniques, exploring various features' impact, optimizing models, and identifying key factors influencing churn.

Customer churn prediction with Python using synthetic datasets. Includes data generation, feature engineering, and training with Logistic Regression, Random Forest, and Gradient Boosting. Improved pipeline applies hyperparameter tuning and threshold optimization to boost recall. Outputs metrics, reports, and charts.

MLOps End-to-End: Customer Churn Prediction System

Typescript library to access Faraday's API infrastructure for B2C predictions https://www.npmjs.com/package/@fdy/faraday-js

ProFeld: survival analysis, predictive maintenance, churn analysis, and remaining useful life prediction in Python