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.
Machine learning project for customer churn analysis and predictive classification using Python and scikit-learn.
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.
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).
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.
Churn prediction using Random Forest and Decision Tree Classifiers.
ML project using Logistic Regression, Random Forest, and XGBoost to predict customer churn.
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