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.
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.
Unlock actionable insights and boost customer retention with this Power BI project. Analyze and visualize risk factors to proactively prevent churn. ➡️
An End to End Customer Churn Prediction solution using AWS services.
Typescript library to access Faraday's API infrastructure for B2C predictions
Using an afticial neural network to predict customers who leave the bank.
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.
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.
Welcome to some case study of data science projects - (Personal Projects).
This repository consists of predicting dynamic pricing, churn predictions using sales and marketing data for understanding users' behaviour.
Build an scikit-learn model to predict churn using customer telco data.
Predict customer churn with text and interpretability.
The python notebook is on googles new collabatory tool. Its a churn model being run on 3 different algorithms to compare.
Predicting user churn for a mobile health app called Diabesties. Capstone project for Galvanize Phoenix Data Science Immersive, October 2017.
Developed an end-to-end machine learning solution for predicting employee churn using Azure Databricks, leveraging Spark for data processing, MLflow for managing the ML workflow, and deploying the model using Databricks model serving.
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