Cuana-E2E

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

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

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.

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.

The Complete Journey Dataset: Churn Prediction

Churn Prediction using PySpark

Customer churn prediction is the process of using machine learning models to identify customers who are likely to leave in the near future.

A comprehensive Churn Classification solution aimed at laying out the steps of a classification solution, including EDA, Stratified train test split, Training multiple classifiers, Evaluating trained classifiers, Hyperparameter tuning, Optimal probability threshold tuning, model comparison, model selection and Whiteboxing models for business sense. (Python)

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