churn-user-prediction

通过游戏埋点数据挖掘进行游戏用户流失分析

Predicting user churn for a mobile health app called Diabesties. Capstone project for Galvanize Phoenix Data Science Immersive, October 2017.

This repository holding a case study on analysis churn in Telecom and building a machine learning model to classify the customer who is likely to churn, which includes EDA, Prediction Model Building, Presentation PDF.

Machine-Learning-1

Build a model predicting which customers are likely to cancel subscription by analysis of their usage and habbits

Importance of churn Analysis and some concept upon it

DissertationCA2

Customer Churn Analysis and Prediction

사용자 분석을 위한 SQL 및 실제 게임 데이터 분석

🏦 Machine Learning model that predicts whether a bank customer is likely to churn, helping identify customers at risk of leaving. 📊 Data-driven customer churn prediction system built with Python and Machine Learning.

through this app we can predict whether our customer will prolong in our bank or not through their records .

End-to-end Customer Churn Intelligence system using Python, Machine Learning, and Power BI. Includes data cleaning, statistical analysis, feature engineering, risk-based customer segmentation, predictive modeling (Logistic Regression, Random Forest, XGBoost), and dashboard-ready analytics for proactive retention strategies.

Built a logistic regression based predictive model to identify customers at high risk of churn and identify the main indicators of churn.

2023년 11월 대한산업공학회(UNIST) : 다중 역할 경험을 고려한 게임 유저 이탈 예측: 롤 게임을 중심으로, 1저자

Different methods for churn prediction

R code to predict booking destination of new user

Predict users churn for a music box app.

MS, Data Analytics Capstone (Nexford University) by Saheed Badru

Predicting Teleco churn dataset from Kaggle. In this repo, I take 3 solution notebooks and combine the analysis into one with some updates.

A comprehensive project predicting customer churn for a telecommunications company using Logistic Regression, Decision Trees, and Random Forest models. Includes data preprocessing, feature engineering, model evaluation, and result visualization to provide actionable insights for customer retention.