{"repo":"deaneeth/telco-churn-mlops-pipeline","free":true,"listed":false,"github":"https://github.com/deaneeth/telco-churn-mlops-pipeline","clone":"git clone https://github.com/deaneeth/telco-churn-mlops-pipeline.git","description":"A end-to-end 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.","language":"Jupyter Notebook","stars":17,"topics":["airflow","apache-spark","churn-prediction","data-science","flask-api","mlflow","mlops","scikit-learn","telco","docker"],"license":"MIT","category":"data-pipelines","readme_excerpt":"📊 Telco Customer Churn Prediction - Production MLOps Pipeline Production MLOps Pipeline with Kafka Streaming & Airflow Orchestration A production-grade MLOps pipeline for predicting customer churn in the telecommunications industry, featuring end-to-end automation, experiment tracking, distributed training, and containerized deployment. Quick Start • Features • Architecture • Documentation • Results Telco Customer Churn Dataset from Kaggle --- 🚀 Quick Start (60 seconds) Get the full pipeline running in under a minute: 💡 Tip: For detailed setup instructions, see Installation Guide --- 📖 Overview This project implements a production-grade MLOps pipeline for predicting customer churn in the telecommunications industry. It addresses a critical business problem: telecom companies lose 26.5% of customers annually, costing billions in revenue. 🎯 What This Project Does - Predicts churn risk for 7,043 telecom customers using ML (84.66% ROC-AUC) - Streams data through Apache Kafka for real-time inference (8.2ms latency) - Orchestrates workflows with Apache Airflow for automated retraining - Tracks experiments using MLflow with 15+ model versions - Deploys containerized REST API for production inference 💼 Business Impact Metric Value Impact -------- ------- -------- Baseline Churn Rate 26.5% Industry standard Model Recall 80.75% Catch 4 out of 5 churners Annual Savings +$220,000 Based on LTV analysis Retention Cost $50/customer vs. $2,000 acquisition 🧠 Learning Outcomes This proj","default_branch":null,"files":null,"tree":[],"storefront":"/r/deaneeth","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/deaneeth/telco-churn-mlops-pipeline/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}