In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.

Bank customers churn dashboard with predictions from several machine learning models.

In the customer management lifecycle, customer churn refers to a decision made by the customer about ending the business relationship. It is also referred as loss of clients or customers. Customer loyalty and customer churn always add up to 100%. If a firm has a 60% of loyalty rate, then their loss or churn rate of customers is 40%. As per 80/20 customer profitability rule, 20% of customers are generating 80% of revenue. So, it is very important to predict the users likely to churn from business relationship and the factors affecting the customer decisions. We are going to show how logistic regression model using R can be used to identify the customer churn in the telecom dataset.

Business Case Study to predict customer churn rate based on Artificial Neural Network (ANN), with TensorFlow and Keras in Python. This is a customer churn analysis that contains training, testing, and evaluation of an ANN model. (Includes: Case Study Paper, Code)

This repo contains regression and classification projects. Examples: development of predictive models for comments on social media websites; building classifiers to predict outcomes in sports competitions; churn analysis; prediction of clicks on online ads; analysis of the opioids crisis and an analysis of retail store expansion strategies using Lasso and Ridge regressions.

This project focuses on predicting customer churn for a bank using machine learning. By analyzing features like credit score, age, balance, and geography, the model identifies at-risk customers, uncovers key churn drivers, and provides insights to help the bank implement targeted retention strategies.

This open source benchmarking framework allows you to build your own P2P learning algorithm and evaluate it in a simulated but realistic -- where you can model message delay, drop or churn -- networked environment. Moreover it contains the prototype implementations of some well-known machine learning algorithms like SVM and Logistic Regression.

A Python-based project for analyzing customer churn using data visualization and machine learning models to predict churn probability. Employs libraries like Pandas, Scikit-learn, and Matplotlib for data preprocessing, model training, and insightful visualizations.

Predicting which set of the customers are gong to churn out from the organization by looking into some of the important attributes and applying Machine Learning and Deep Learning on it.

This project involves predicting customer churn in a telecommunications company using machine learning techniques, exploring various features' impact, optimizing models, and identifying key factors influencing churn.

Customer analytics has been one of hottest buzzwords for years. Few years back it was only marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza. SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services. In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics. By the late 2000s, Facebook, Twitter and all the other socialchannels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant. With the digital age things have changed drastically. Customer issuperman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience. This tsunami of data has changed the customer analytics forever. Today customer analytics is not only restricted to marketing forchurn and retention but more focus is going on how to improve thecustomer experience and is done by every department of the organization. A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics. From the technology perspective, the biggest change is the introduction of big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation. Then came Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure. Predictive models of customer churn, Retention, Cross-Sell do exist today as well, but they run against more data than ever before. Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical. There are various ways customer analytics is carried out: Acquiring all the customer data Understanding the customer journey Applying big data concepts to customer relationships Finding high propensity prospects Upselling by identifying related products and interests Generating customer loyalty by discovering response patterns Predicting customer lifetime value (CLV) Identifying dissatisfied customers & churn patterns Applying predictive analytics Implementing continuous improvement Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time. Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect. Tomorrow there may not be just plain simple customer sentiment analytics based on feedback or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time. There’s no doubt that customer analytics is absolutely essential for brand survival.

End to end projects-- Customer Churning prediction using Gradient Boost Classifier Algorithm perform pre-processing steps then fit data into the Algorithm and Hyper Parameter Tunning to reduce TN & FN value to perform our model to works with a new data. Finally deploying the model using Flask API

Developed a churn prediction model using XGBoost, with comprehensive data preprocessing and hyperparameter tuning. Applied SHAP for feature importance analysis, leading to actionable business insights for targeted customer retention.

Churn analysis library

🎓📚📈 Collection of scientific publications that explore, model and predict customer churn and lifetime value (CLV)

Repositório para o #alurachallengedatascience1

This repository will have all the necessary files for machine learning and deep learning based Banking Churn Prediction ANN model which will analyze tha probablity for a customer to leave the bank services in near future. Deployed on Heroku.

Build an scikit-learn model to predict churn using customer telco data.

Infuse AI into your application. Create and deploy a customer churn prediction model with IBM Cloud Private for Data, Db2 Warehouse, Spark MLlib, and Jupyter notebooks.

Sample for building a churn prediction model with GraphLab Create

A deep exploration of loyalty as a multi-dimensional behavioral system shaped by intent, habit, and sensitivity. This article introduces a geometric framework for modeling customer behavior, predicting churn trajectories, and designing ML systems that understand loyalty as a dynamic state, not a metric.

Next-generation analytics & ML-powered churn prediction for Solana gaming. Self-training models predict player churn 14 days in advance. Live dashboard + REST API analyzing 60M+ on-chain transactions across 12 games.

A production-grade MLOps pipeline for predicting telecom customer churn, featuring automated data preprocessing, ML model training, experiment tracking with MLflow, distributed training using PySpark, real-time inference via Kafka streaming, Airflow DAG orchestration, and Dockerized REST API deployment.

Build a classification model for reducing the churn rate for a telecom company

Analysis and Prediction of the Customer Churn Using Machine Learning Models (Highest Accuracy) and Plotly Library

Implementation of Decision Tree Classifier, Esemble Learning, Association Rule Mining and Clustering models(Kmodes & Kprototypes) for Customer attrition analysis of telecommunication company to identify the cause and conditions of the churn.

Predict and prevent customer churn in the telecom industry with data-driven insights. This project explores customer behavior, builds predictive models, and offers recommendations to reduce attrition rates. Explore the code for analysis, model building, and more.

End-to-end Customer Churn Prediction system showcasing real-world ML skills. Built with XGBoost, SMOTE, and Streamlit, featuring a deployable UI, probability-based risk insights, and production-ready architecture. Demonstrates modeling judgment, deployment, and business impact.

Modeling customer churn with Spark

Predict the customers who are likely to churn for an European bank using various models- KNN, logistic regression, decision tree and random forest