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.

Production-ready customer churn prediction API built with FastAPI, scikit-learn, and Docker.

This project uses machine learning to predict customer churn in the Banking sector. It covers the end-to-end process, from data ingestion, validation, and transformation to model training and deployment using FastAPI. The system includes real-time predictions and provides an API for customer churn analysis.

End-to-end Customer Churn Prediction using Machine Learning and Streamlit

An AI-powered customer retention intelligence system that predicts churn risk, explains key drivers, and recommends retention actions using UI and real AI reasoning.

This project uses machine learning techniques to analyze customer behavior and predict churn probability, helping businesses identify at-risk customers and take proactive retention measures.

End-to-end customer churn analysis using SQL, Python, and Power BI to identify churn drivers and predict high-risk customers using Random Forest.

Enterprise-grade Telecom Customer Churn Prediction system blending advanced machine learning (XGBoost), real-time Flask API deployment, and interactive Streamlit dashboards to enable data-driven customer retention strategies.

Predict customer churn with XGBoost ML model. Analyze churn drivers, segment customers by risk, and get AI-powered retention strategies. Interactive Streamlit dashboard included.

a complete machine learning system for predicting the churn probability of customers for a telecommunications company.

End-to-end customer churn prediction pipeline using XGBoost, Optuna hyperparameter tuning, probability calibration, and SHAP explainability — with a full inference module for real-time predictions and business-ready reports.

End-to-end MLOps pipeline for customer churn prediction using a LightGBM + XGBoost + CatBoost ensemble, served via FastAPI with MLflow tracking and MinIO artifact storage, fully containerized with Docker.

End-to-end churn prediction system with ML models, explainability (SHAP), FastAPI deployment, and Streamlit dashboard for business insights.

AI-powered customer churn prediction tool for eCommerce businesses. Built with Streamlit, featuring interactive visualizations, multi-format exports, and sample datasets.

End-to-End Customer Churn Prediction Machine Learning Project using Python and Streamlit

This repository contains a comprehensive machine learning project for customer churn prediction, featuring data processing pipelines, model development with hyperparameter tuning, and insightful data visualization.

Machine learning dashboard for predicting customer churn and analyzing retention insights.

Customer Churn Prediction using Machine Learning with Python, Pandas, Scikit-learn, Random Forest, Logistic Regression, and Gradient Boosting.

Automated ML pipeline for preprocessing, training, evaluating, and tracking customer churn prediction models using MLflow and configurable algorithms like XGBoost. Designed for scalability and easy customization to fit structured banking data workflows.

MLflow-based Telco Customer Churn Prediction system that tracks, compares, and stores machine learning experiments. Built using Python, Scikit-learn, and MLflow to demonstrate real-world MLOps workflows for model training, evaluation, and experiment tracking.