customerretention

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.

Data Science and Machine Learning Internship at Flip Robo Technologies

This project develops a machine learning model to predict customer churn for a California-based telecom company using data from 7043 customers. Our goal is to enhance customer retention strategies through detailed data analysis and feature engineering.

Completed PwC Power BI Virtual Internship via Forage, building interactive dashboards on churn, customer service, and diversity using DAX, Power BI, and real-world business scenarios.

Customer Retention & Churn Analysis using Power BI to identify high-risk customer segments, tenure-based churn patterns, and key retention drivers with actionable business insights.

The "Churn Prediction" project analyzes customer data to identify factors leading to churn 📉🤔. Using machine learning algorithms, it predicts which customers are likely to leave, enabling businesses to implement targeted retention strategies and improve customer satisfaction.