Determining the churn rate of a bank and predicting which of their customers are at high risk of leaving the bank.

Predict Churn in Telecom Industry Using Logistic Regression with R

A model to predict customer churn using Spark

Churn prediction for bank customers

Churn prediction based on bank customers

Performed a churn analysis on a Kaggle competition - Customer Churn Prediction 2020 to predict whether a customer will change telco provider

predicting churn on a Telecom company

The purpose of this project is to use SQL to transform multiple datasets relating to customer phone calls over a four month period, to engineer new features, and to combine the datasets into a suitable case table in order to use Machine Learning techniques to predict the likelihood of a customer churning in any given month.

[124th Place] Repository for Challenge 05 - SONDA of the IBM Maratona Behind the Code 2021

Predicting customer churn for the music app, Sparkify, using PySpark on AWS EMR clusters

This project is an attempt to create a Churn Customer Classification models, resulting in balanced Logistic Regression model with 80% recall and 73% precision value, also Decision Tree with high recall value (91%). Web application included.

Uma empresa de telecomunicações que fornece serviços está preocupada em reduzir a taxa de retenção de seus clientes. Portanto, o gerente de CRM me contratou para que eu desenvolva um modelo de previsão de clientes que provavelmente irão parar de utilizar os serviços da empresa.

Customer Churn

A prediction model based on ML as well as DL and compare their performances to find Churned Customers

The telecom operator Interconnect would like to forecast churn of their clients. To ensure loyalty, those who are predicted to leave will be offered promotional codes and special plans.

This project involves performing customer segmentation and RFM (Recency, Frequency, Monetary) analysis on customer data from a retail company. The primary goal is to categorize customers into segments based on their buying behavior and identify potential target groups for marketing campaigns.

Churn prediction aims to identify customers who are likely to cancel/switch their accounts based on their characteristics and behavior patterns. This helps banks prioritize retention efforts.

Data Science Challenge from Coursera Project : Churn Prediction