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

Two differrent approach to predict Churn customers and finding out important variables that drives churn

A data-driven approach with explainable artificial intelligence for customer churn prediction in the telecommunications industry

Telecom churn prediction based webapp

Analysing customer-level data of a leading telecom firm, building predictive models to identify customers at high risk of churn and identifying the main indicators of churn.

Supervised learning algorithm was used to build churn prediction model to help solve a telecoms company's customer churn problem.

This is a Streamlit web application for predicting Telecom Churn. The app uses a trained machine learning model to predict whether a customer is likely to churn or not based on certain input features.

In this project, you will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

Analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn (usage-based churn) and identify the main indicators of churn.

Derive insights of factors contributing to customer churn in the Telecom Industry.

Built an end-to-end machine learning project to predict customer churn in the telecom domain. Performed data preprocessing and exploratory data analysis to understand customer behavior and key churn drivers. Developed and compared a Logistic Regression model (Scikit-learn) with a Neural Network model (PyTorch), converting NumPy arrays to tensors.

A simple beginner-friendly machine learning-based customer churn prediction project

A machine learning project to predict customer churn in the telecom industry using Logistic Regression. The project analyzes customer behavior and subscription data to identify factors contributing to churn, providing actionable insights for retention strategies.

This project involved analyzing 10,000 customer records, applying data preparation techniques, and training supervised machine learning models, achieving 94% accuracy. Model efficiency was further refined using cross-validation and hyperparameter tuning, ensuring reliability and performance

Telecom Churn Prediction Model using ANN

upGrad's Telecom Churn Case Study hosted on Kaggle platform

Designing strategies to pull back potential churn customers of a telecom operator by building a model which can generalize well and can explain the variance in the behavior of different churn customer. Analysis being done on large dataset which has lot of scope for cleaning and choosing the right model for prediction.

This repository uses Machine Learning models to gather insights on Customer Retention rate in Telecom Industry.

End-to-end Power BI dashboard analyzing customer retention across 7,000+ telecom accounts. Features DAX-driven churn metrics, cohort segmentation, and actionable insights on "Month-to-Month" contract risks and support-ticket correlations. Designed for executive storytelling and strategic churn reduction.