This project focuses on predicting customer churn using machine learning algorithms. By analyzing historical customer data, the model aims to identify patterns that indicate a customer is likely to stop using a service, enabling businesses to take proactive measures to retain valuable customers.

TD Bank-Real Time Churn Insights with Robust Machine Learning Models and Interactive Web Deployment

This repository contains the data, code, and documentation for a project to analyze and predict churn in PowerCo's SME customer segment. The project includes data exploration, cleaning, and transformation, as well as the development and evaluation of a machine learning model to predict churn based on price sensitivity and other relevant factors.

The "Churn Prediction" project analyzes customer data to identify factors leading to churn 📉🤔. Using machine learning algorithms, it predicts which customers are likely to leave, enabling businesses to implement targeted retention strategies and improve customer satisfaction.

This project develops a machine learning model to predict customer churn for a California-based telecom company using data from 7043 customers. Our goal is to enhance customer retention strategies through detailed data analysis and feature engineering.

Official implementation of "Next-Gen Customer Retention". A Stacked Ensemble Churn Prediction model achieving 98.1% accuracy. Introduces the Latency Aware Accuracy Index (LAAI) for real-time efficiency. 📄 Published in SES Journal (2025).

ML project using Logistic Regression, Random Forest, and XGBoost to predict customer churn.

Churn prediction using Random Forest and Decision Tree Classifiers.