customerchurnprediction

This interactive web application leverages machine learning to predict whether a telecom customer is likely to churn. Users can input customer details for real-time predictions or upload a CSV file for batch analysis.

End-to-End Retail Customer Churn Prediction using Gradient Boosting and Streamlit. This repository showcases a comprehensive data science workflow, from feature engineering with RFM to building a Gradient Boosting model and deploying an interactive dashboard for actionable customer retention insights.

Predicts telecom customer churn in real-time using XGBoost with 86% ROC-AUC | Features EDA, model comparison (LR vs RF vs XGBoost), threshold tuning & interactive Streamlit UI | Deployed on Streamlit Cloud

End-to-end customer churn analysis with EDA, SQL, Streamlit dashboard and ML prediction

ML project to predict telecom customer churn using Logistic Regression and XGBoost models .

Machine Learning web app that predicts telecom customer churn with 80.4% accuracy using Logistic Regression and Streamlit

The Customer Churn Prediction System is a Machine Learning project designed to predict whether a customer is likely to leave a service based on historical customer information. The application uses a trained Random Forest Classifier and provides predictions through an interactive Streamlit dashboard.

End-to-end customer churn prediction project using machine learning with business insights and deployment-ready pipeline.

Predicting customer churn using Random Forest machine learning model on Telco dataset.

An end-to-end machine learning system for predicting customer churn, featuring a full data pipeline, experiment tracking with MLflow, a REST API built with FastAPI, and Docker-based deployment.

The goal of this project is to predict the likelihood of a Telco customer churning using Machine Learning techniques with Python and TensorFlow. This allows the company to implement retention strategies and reduce customer loss.

CODSOFT Machine Learning Internship Tasks

End-to-End Customer Churn Prediction using Machine Learning, Data Analysis and Business Insights.

Machine Learning project for predicting bank customer churn using classification algorithms.

End-to-end customer churn intelligence platform featuring data cleaning, exploratory analysis, temporal churn modeling, behavioral feature engineering, Random Forest classification, FastAPI REST API, and an interactive Streamlit dashboard with cloud deployment.