WTTE-RNN a framework for churn and time to event prediction
Popular collection of practical Python notebooks for machine learning, deep learning, time series, generative AI, and analytics.
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.
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.
Welcome to some case study of data science projects - (Personal Projects).
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.
Typescript library to access Faraday's API infrastructure for B2C predictions https://www.npmjs.com/package/@fdy/faraday-js
Churn analysis library
This repository contains the projects I worked on during my Codsoft internship. Each project focuses on a different machine learning problem, including credit card fraud detection, customer churn prediction, movie genre classification, and SMS spam detection.
Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.
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.
🦧 Customer 🦁 Churn 🐯 Prediction 🐸 Machine 🐳 Learning 🐲 is 🌺 a 🏵 data 🪰 science 🐠 focused 🐊 on 🦥 predicting 🏘 whether 🏭 customers 🏪 are 🏟 likely 🏣 to 🏥 leave 🏦 a 🏨 service 🕌 algorithms 🕍 analyzing 🚄 historical 🚋 customer 🚟 data 🚠 the 🚁 system 🛸 identifies 🚝 patterns 🏜 behaviors ⚽ that ⚾ indicate 🏀 potential 🏐
Next-generation analytics & ML-powered churn prediction for Solana gaming. Self-training models predict player churn 14 days in advance. Live dashboard + REST API analyzing 60M+ on-chain transactions across 12 games.
Build an scikit-learn model to predict churn using customer telco data.
A end-to-end 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.
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