Customer Churn End2End Machine learning project

Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. With the help of ML classification algorithms, we are going to predict the Churn.

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This solution combines Amazon Pinpoint with Amazon SageMaker to help automate the process of collecting customer data, predicting customer churn using ML, and maintaining a tailored audience segment for messaging.

A deep exploration of loyalty as a multi-dimensional behavioral system shaped by intent, habit, and sensitivity. This article introduces a geometric framework for modeling customer behavior, predicting churn trajectories, and designing ML systems that understand loyalty as a dynamic state, not a metric.

A production-grade 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.

Customer Churn Analysis Experiments using Classical ML algorithms and Deep Neural Network

End-to-end Customer Churn Prediction system showcasing real-world ML skills. Built with XGBoost, SMOTE, and Streamlit, featuring a deployable UI, probability-based risk insights, and production-ready architecture. Demonstrates modeling judgment, deployment, and business impact.

Bank customer churn prediction using multiple ml models

A full ML pipeline for customer churn prediction in telecom, banking, or SaaS. Includes robust data cleaning, automatic feature engineering, model training/tuning (Logistic Regression, RF, XGBoost), interpretability, and interactive dashboards for actionable business retention insights.

Telco Churn Analysis and Modeling is a comprehensive project focused on understanding and predicting customer churn in the telecommunications industry. Utilizing advanced data analysis and machine learning techniques, this project aims to provide insights into customer behavior and help develop effective strategies for customer

Given a dataset from Kaggle. EDA + ML was utilized to predict customer churn. Deliverables include a written report, dashboard, and application.

A production-grade end-to-end ML system for predicting customer churn in retail banking. It uses an XGBoost model optimized for top-K targeting, with full lifecycle support including data validation, experiment tracking (MLflow), automated monitoring, drift-based retraining, and explainability via SHAP.

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Experiments and deployment of ML models for churn prediction and customer retention strategy.

The objective is to build a classifier for prediction of customer churn.

This project predicts customer churn using a Telco dataset with machine learning models like Logistic Regression, Random Forest, and XGBoost. It involves data preprocessing, exploratory analysis, and balancing data using SMOTE. Model performance was evaluated using metrics such as accuracy, precision, recall, and F1-score.

ML project Predicting Customer Churn for a Gym

Project Predict Customer Churn of ML DevOps Engineer Nanodegree Udacity

ML-powered web app that predicts telecom customer churn using Random Forest — built with Flask, Scikit-Learn & Pandas

A fully automated ML pipeline for customer churn prediction in telecom, orchestrated with Apache Airflow. Covers data ingestion, validation, feature engineering, model training, deployment, and monitoring with DVC-based versioning for complete reproducibility.

Customer Churn Prediction ML Project

A machine learning project that predicts customer churn using the Telco dataset.

Step-by-step EDA and data preprocessing journey for customer churn prediction. Updated weekly with raw & processed datasets, notebooks, and ML-ready pipeline.

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Customer Churn Prediction using Machine Learning. Built an end-to-end ML pipeline with data preprocessing, EDA, feature engineering, and classification models (Decision Tree, Random Forest, XGBoost) to predict customer churn and identify key factors affecting customer retention.

This repository contains an end-to-end ML lifecycle demo using the Azure Maching Learning Studio.

Data analysis, data pre-processing, building prediction models with machine learning models.

This is ML Project to find Churn Of a customer in telecome industry