customer-churn

Unlock actionable insights and boost customer retention with this Power BI project. Analyze and visualize risk factors to proactively prevent churn. ➡️

Customer churn prediction with Python using synthetic datasets. Includes data generation, feature engineering, and training with Logistic Regression, Random Forest, and Gradient Boosting. Improved pipeline applies hyperparameter tuning and threshold optimization to boost recall. Outputs metrics, reports, and charts.

Source-driven decision hub inspired by the PwC Switzerland / Forage Power BI virtual case experience, deployed on GitHub Pages.

Analysis and Prediction of the Customer Churn Using Machine Learning Models (Highest Accuracy) and Plotly Library

Unified ML platform serving two production risk models behind one API, fraud detection using a Logistic Regression pipeline at 92.4 percent accuracy and 0.90 F1, and churn prediction using a five-estimator hard-voting ensemble at 85.6 percent accuracy, with SMOTE balancing, sub-0.5 second inference, and CI-enforced 90 percent test coverage.

Free, open-source churn prediction for SaaS - plug in your data and know who's leaving before they do

Machine Learning, EDA, Classification tasks, Regression tasks for customer churn

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

CyberSoft Machine Learning 03 - Evaluate

Modelo predictivo de abandono de clientes. EDA + ML para retención proactiva

Customer Churn Prediction (End-to-End ML Pipeline)

Analyze your customer database with ease

Cloud-native MLOps platform for customer churn prediction using FastAPI, MLflow, Docker, Kubernetes, and GitHub Actions.

Customer churn analysis project using Excel and Power BI. This project investigates the exit patterns of banking customers using various demographics and behavior indicators such as age, gender, credit card status, geography, and credit score. Insights help identify key drivers of churn and guide retention strategies.

In this BI consultancy project, I advised the CMO of Maven Communications on how to reduce customer churn, using data.

Customer churn prediction system using XGBoost, SHAP explainability, and Streamlit for real-time telecom retention analysis.

📂 Task's and work completed during my internship at Saiket_System, focusing on Data Science.🧑‍💻

Customer churn analysis using Python, logistic regression and gradient boosting.

This is a Machine Learning + Flask Web App that predicts whether a customer is likely to churn and suggests a discount policy based on churn probability.

An end-to-end cost-sensitive customer churn prediction system that combines machine learning, business cost optimization, threshold tuning, and an interactive Streamlit dashboard to prioritize customer retention and reduce potential revenue loss.