Developed a churn prediction model using XGBoost, with comprehensive data preprocessing and hyperparameter tuning. Applied SHAP for feature importance analysis, leading to actionable business insights for targeted customer retention.

Call Detail Record (CDR) is the information captured by the telecom companies during the Call, SMS and Internet activity. These information’s provides insights about the customer needs when it is used with customer demographics. Most of the telecom companies using call detail record information in the fraud detection by clustering the user profiles, Customer churn by usage activity and targeting the profitable customers by using RFM analysis.

Telecom Customer Churn Analysis with Python

Build a classification model for reducing the churn rate for a telecom company

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.

Predict and prevent customer churn in the telecom industry with data-driven insights. This project explores customer behavior, builds predictive models, and offers recommendations to reduce attrition rates. Explore the code for analysis, model building, and more.

The Telecom Churn Analysis is a personal collection of simple and useful Python scripts created to improve programming skills and solve everyday problems using code. Each script focuses on a different concept such as file handling, data processing, automation, or logic building.

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Churn Analysis - Telecom Dataset

Telecom Customer Churn Analysis using Power BI This project analyzes telecom customer data to identify key factors influencing customer churn. It includes interactive Power BI dashboards highlighting churn trends, customer behavior, and actionable insights to improve retention and business performance.

Telecom customer churn analysis

Supervised Prediction of churn rate of a Telecom Company. Depending on his usage patterns and plans bought, classifying users if they would churn in future or not

Telecom Customer Churn Analysis & Prediction project uses Gradient Boosting for precise predictions, Power BI for churn pattern visualizations, and Streamlit for interactive insights. With robust code and meticulous data preprocessing, stakeholders access accurate predictions to optimize retention and drive profitability.

Power BI project analyzing telecom customer churn to uncover insights that help reduce customer loss and improve satisfaction.

This project demonstrates churn analysis for telecom industries. Mobile carriers are first priority to monetize rich supplies of customer information—while being mindful of legal and privacy issues. When data assets are transformed into new revenue streams, next-generation analytics will become integral to high performance. The aim of churn analysis is to identify customers who are about to transfer their business to a competitor.

For Upx Academy, one of our multiple projects were telecom Customer Churn, We were provided Customer Usage Data as input. On the basis of that we did Telecom Churn Analysis and created a churn prediction model.

An interactive Streamlit web app that visualizes customer churn patterns using Seaborn and Matplotlib. Includes insights by gender, tenure, contract type, services, and payment methods to help telecom companies reduce churn and improve retention.

Telecom Customer Churn Analysis Dashboard built in Power BI to identify key churn drivers and customer behavior patterns. The report analyzes service usage, contract types, payment methods, and demographics to highlight high-risk segments and support data-driven retention strategies.

customer churn prediction prevention in telecom industry using machine learning and survival analysis

Subscriber churn rate Prediction for Telecom Company - using Logistic Regression analysis. Target segmentation for retention campaign & Revenue maximization forecast

Prediction of propensity to churn from the usage behavior using logistic regression.

Churn Analysis of Telecom data from VDM

This project analyzes customer churn in a telecom company using data visualization and preprocessing techniques. It identifies key factors like senior citizen status impacting churn, with 26.5% overall churn rate. The goal is to help improve customer retention by understanding churn behavior through data insights.

Telecom Customer Churn Analysis Dashboard built using Power BI. This project analyzes customer behavior and identifies churn patterns using interactive visualizations.

This repository showcases a Python project using Machine learning Algorithms/ Models on Churn Analysis in the telecommunication industry. By leveraging Machine Learning techniques, I strive to help telecom providers reduce churn rates, improve customer retention, and make data-driven decisions.

This project predicts Telecom customer churn by combining customer data and emotional analysis from voice interactions.Our goal is to identify at-risk customers and enhance customer retention strategies by understanding both behavioural patterns and emotional tone.

Almabetter certified Python EDA project

This project focuses on segmenting customers based on their tenure, creating "cohorts", allowing us to examine differences between customer cohort segments and determine the best tree based ML model.

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A Power BI project that analyzes telecom customer churn to identify why customers leave and how to reduce churn. Includes insights on contract types, internet services, payment methods, monthly charges, and tenure, along with actionable recommendations to improve customer retention.