End-to-end customer churn prediction pipeline using Spark.

End-to-end churn prediction pipeline using XGBoost and MLflow. Features automated preprocessing, threshold optimization (F1: 0.637), and imbalance handling (scale_pos_weight). Optimized for 81% recall to maximize customer retention.

End-to-end MLOps pipeline for churn prediction in live games.

Machine Learning Classification Projects – Research-oriented Python portfolio featuring end-to-end workflows for income prediction, fraud detection, tumor classification, churn, and more, with reproducible pipelines, EDA, and model evaluation.

End-to-End Customer Churn Prediction System | XGBoost + SHAP | FastAPI Backend | Streamlit UI | Power BI Analytics | Production-Ready ML Pipeline

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.

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"End-to-end churn prediction and uplift pipeline for profitable customer retention campaigns."

End-to-end MLOps pipeline for automated concept drift detection, model retraining, and churn prediction.

End-to-end customer churn prediction pipeline with Logistic Regression, including comprehensive EDA, class imbalance handling, threshold tuning, and model interpretability.

📈 Build a production-ready churn prediction system with an end-to-end MLOps pipeline using FastAPI, scikit-learn, and automated CI/CD practices.

End-to-end machine learning project for Telco Customer Churn Prediction. Includes data preprocessing, feature engineering, model evaluation, hyperparameter tuning, cross-validation, and final model deployment-ready pipeline.

End-to-end churn prediction case study for retail banking, combining a leakage-safe ML pipeline, model comparison, and threshold-based targeting with scenario-driven revenue impact simulation to translate predictions into actionable retention decisions.

Built an end-to-end pipeline for a customer churn prediction model, trained five different ML models, performed hyperparameter tuning and served the model for inference through a web app

🚀 End-to-end ML project: 93% accurate churn prediction + customer segmentation with Random Forest & K-Means. Features automated pipeline, Streamlit dashboard, comprehensive testing, and cross-platform deployment. Production-ready with 505K+ customer dataset analysis.

An end-to-end machine learning project predicting bank customer churn with a Gradient Boosting Classifier. It features a complete pipeline for data processing, model training, and real-time predictions via a Flask API. SMOTE is used for handling imbalanced data, and MLflow is integrated for model tracking.

An end-to-end machine learning pipeline that predicts customer churn using the Telco dataset. Built with a clean Cookiecutter project structure. It includes EDA, preprocessing, and a Random Forest model hitting ~88% test accuracy. The app is fully containerized with Docker, CI/CD-ready using GitHub Actions + Render for automatic deployment.

This project builds a fully automated, end-to-end data engineering and data science pipeline to predict customer churn using AWS Free Tier services.

End-to-end ML pipeline for customer churn prediction with Streamlit dashboard

End-to-End Machine Learning Pipeline for Customer Churn Prediction with API Deployment.

End-to-End AI-powered Customer Churn Prediction Pipeline, integrating data engineering and AI

End-to-End Customer Churn Prediction Pipeline using Python (Random Forest) and Power BI

End-to-end churn prediction system with training pipeline, evaluation, model serving, and batch scoring.

An end-to-end data pipeline for customer churn prediction using Python, Flask, and Snowflake.

End-to-end ML pipeline for customer churn prediction with EDA, model comparison, and business insights

End-to-end customer churn prediction pipeline with MLflow experiment tracking, model comparison, and Dockerized deployment.

Production-grade end-to-end machine learning pipeline for customer churn prediction with real-time deployment.

End-to-end churn prediction on the IBM Telco dataset (EDA, pipelines, threshold tuning, PR-AUC/F2).

End-to-end customer churn prediction pipeline with XGBoost optimization, SHAP explainability and interactive Streamlit dashboard deployment.

End-to-end pipeline and web app for a customer churn prediction model using machine learning in Python.