This is a sample code repository of the telco customer churn analysis or prediction by the classification/regression model for experiment and learning purposes.

Prediction of whether or not a customer leaves in an specific period of time, deployed to GCP

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

Predicting customer churn using ANN and dealing with imbalanced data.

Gaussian mixture model for predicting customer churn

Customer Churn Prediction in Telecom Industry where customers are choose from a variety of service provider and actively switch from one to next. with the help of Machine Learning Classification Algorithm we are going to predict the churn.

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

The analysis evaluates Campaigns' ROI within Regork's (imaginary company) least profitable departments, offering strategic insights to enhance marketing efforts and drive profitability.

Customer churn prediction is to measure why customers are leaving a business. We will build a deep learning model to predict the churn.

This project leverages machine learning techniques to analyze historical customer data and build a predictive model.

This project provide a template of the traditional binary classification model. Feel free to check the detailed steps of the whole process machine learning modelling.

Employ Artificial Neural Networks for Customer Churn Prediction, empowering businesses to proactively retain customer loyalty and optimize strategies. Utilize advanced analytics to foresee and address potential attrition seamlessly.

Customer Churn Prediction is a machine learning project aimed at predicting whether a specific user will leave a service or not. The project involves extensive exploratory data analysis (EDA), model training and deployment of a Streamlit web application for user interaction.

This project aims to Predict customer churn in subscription business using ML. Dataset has usage behavior, demographics, churn status. Trained Logistic Regression, Random Forest, Gradient Boosting. Best model deployed for future churn prediction.

In this project, we have worked with hundreds of anonymized features to predict if a customer is satisfied or dissatisfied with their banking experience.

Predicting customer churn using machine learning algorithms

A classification model to predict customer churn in Telecomunication Company

Developed an end-to-end machine learning model to predict credit card customer churn. (All stages including ingestion, EDA, feature engineering, normalization, and scaling, train-validation-split & deployment)