📱 Customers are likely to leave a telecom service, enabling companies to take measures for retention and create accurate churn prediction models.

This Excel workbook analyses the customer churn in a telecommunications company focusing on demographic factors, data consumption patterns, and contract types. The goal is to identify key drivers of churn and provide actionable insights to improve customer retention.

This project conducts an exploratory data analysis (EDA) on a Telco customer churn dataset. It visualizes key factors influencing customer churn, including payment methods, contract types, and service usage. The insights gained aim to help businesses understand customer retention and develop strategies to reduce churn rates.

An end-to-end machine learning project predicting bank customer churn with a Gradient Boosting Classifier. It features a complete pipeline for data processing, model training, and real-time predictions via a Flask API. SMOTE is used for handling imbalanced data, and MLflow is integrated for model tracking.

Performs customer churn prediction, built using logistic regression

Automated Churn Prediction using Classifier Model and deploy as a Streamlit Web Application

AI-Enhanced Customer Retention System (AIECRS) is an AI-based system designed to predict customer churn and suggest retention strategies.

The project predicts bank customer churn using an Artificial Neural Network (ANN). It includes data preprocessing, model training with TensorFlow and Keras, and deployment via a Streamlit app. The model's performance is visualized using TensorBoard, showcasing effective machine learning techniques for customer retention.

Customer Churn

Telco Customer Churn Prediction project focuses on developing a predictive model to identify customers who are likely to cancel their subscription with a telecommunications company.

The core purpose of this study is to find the impact of Sentiment Analysis in predicting customer churn for the e-commerce industry by employing different predictive models. Furthermore, the study is also focused on observing which model is best in a more accurate prediction for determining the churn rate of customers.

We going to build a basic model for predicting customer churn using Telco Customer Churn dataset. We're using some classification algorithm to model customers who have left, using Python tools such as pandas for data manipulation and matplotlib for visualizations.

This project predicts customer churn using machine learning. It involves data cleaning, EDA, feature engineering, and model evaluation. AdaBoostClassifier with SMOTE was optimized using GridSearchCV and validated with ROC analysis.

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

This project analyzes and predicts customer churn of a music streaming service using Spark on a large dataset.

Tree methods for customer churn prediction. Creating a model to predict whether or not a customer will Churn .

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

The repository presented steps for building a model that predicted whether a customer would switch telecommunication service providers.

End-to-End Machine Learning application to predict the customer churn. machine learning is applied to foresee if customers are likely to leave a service. 🤖💼 This involves analyzing customer data, training a model, and predicting churn probabilities. 🚀📊