Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. With the help of ML classification algorithms, we are going to predict the Churn.

Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.

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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.

A Shiny App for Telecom Customers churn prediction

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A full ML pipeline for customer churn prediction in telecom, banking, or SaaS. Includes robust data cleaning, automatic feature engineering, model training/tuning (Logistic Regression, RF, XGBoost), interpretability, and interactive dashboards for actionable business retention insights.

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

This project focuses on developing a machine learning system to predict customer churn in the telecommunications industry. It covers the entire data science lifecycle, from exploratory data analysis to model deployment, enabling proactive intervention and customer retention

The primary objective of this project is to predict telecom customer churn using data science techniques.

An AI-powered customer churn prediction system for telecom companies, achieving 98.1% accuracy in identifying customers at risk of churning. Built with machine learning and an interactive Streamlit dashboard for real-time insights and predictions.

The "Telecom Customer Churn Prediction " GitHub repository is a project focused on analyzing and predicting customer churn.

Renewal/Churn probability prediction of Telecom Customers, CNN- Handwritten Digit recognition

ChurnNet: Deep Learning Enhanced Customer Churn Prediction in Telecommunication Industry

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.

Telecom Customer Churn with Stats Models

it will calculate the probablity of the customer

Prediction whether a customer will Churn the Telecom Company or not

Customer churn prediction for telecom dataset

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Telecom Customer Churn Prediction.

This project leverages the power of Neural Networks to predict the factors and conditions a customer remain loyal and also why customer is leaving the company. This information helps business house to focus to week links and have more customer conversion

Machine learning project predicting customer churn for a telecom company using Logistic Regression, SVM, Decision Tree, and Random Forest.

🔍 Predict customer churn in the telecom industry using machine learning models like Decision Tree, XGBoost, and SVM. Includes data preprocessing, model training, evaluation, and a FastAPI app for interactive predictions.

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

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.

A fully automated ML pipeline for customer churn prediction in telecom, orchestrated with Apache Airflow. Covers data ingestion, validation, feature engineering, model training, deployment, and monitoring with DVC-based versioning for complete reproducibility.

Developed a churn prediction classification model using various techniques including: EDA, Decision trees, Naive Bayes, AdaBoost, MLP, Bagging, RF, KNN, logistic regression, SVM, Hyperparameter tuning using Grid Search CV and Randomized Search CV.

The goal of this project is to predict customer churn (whether a customer will leave the telecom service) using a model stacking approach. Model stacking involves training multiple models and combining their predictions using another model.

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