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

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.

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.

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.

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

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.

This interactive web application leverages machine learning to predict whether a telecom customer is likely to churn. Users can input customer details for real-time predictions or upload a CSV file for batch analysis.

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

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.

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ChurnGuard is an AI-powered customer churn prediction system where a machine learning model was developed to analyze customer behavior and predict churn risk. The model is integrated with a FastAPI backend and an interactive web dashboard to provide churn probability, customer priority scoring, and actionable retention insights in real time.

Bank customer churn prediction using multiple ml models

A research-driven ML workflow demonstrating data preprocessing, feature engineering, 13+ model experiments, hyperparameter tuning, boosting variations, and performance metrics analysis for real-world churn prediction.

End-to-end platform for training, deploying, and monitoring a churn prediction model—built using MLOps best practices and tools applied from the DataTalksClub MLOps Zoomcamp. Project earned the highest-tier score (achieved by 11 out of 200+ cohort participants) in peer-reviewed project assessment.

A full ML pipeline for customer churn prediction in telecom, banking, or SaaS. Includes robust data cleaning, automatic feature engineering, model training/tuning (Logistic Regression, RF, XGBoost), interpretability, and interactive dashboards for actionable business retention insights.

Companies use different marketing strategies for different customer segments. In order to do so companies use marketing analytics for Analysing Business Metrics, Customer Segmentation, Customer Lifetime Value Prediction, Churn Prediction, Predicting Next Purchase Day, Predicting Sales, Market Response Prediction, Uplift Modelling & A/B Testing Design and Execution.

Telecom Churn Prediction using Machine Learning models

FTI Churn Framework This repository provides a structured and standardized approach to building machine learning workflows for customer churn prediction. Using the FTI (Feature, Training, Inference) architecture, the project demonstrates best practices in data preprocessing, model training, and real-time inference deployment.

Code for the paper "A churn prediction dataset from the telecom sector: a new benchmark for uplift modeling"

Business Problem: Dataset of a bank with 10,000 customers measured lots of attributes of the customer and is seeing unusual churn rates at a high rate. Want to understand what the problem is, address the problem, and give them insights. 10,000 is a sample, millions of customer across Europe. Took a sample of 10,000 measured six months ago lots of factors (name, credit score, grography, age, tenure, balance, numOfProducts, credit card, active member, estimated salary, exited, etc.). For these 10,000 randomly selected customers and track which stayed or left. Goal: create a geographic segmentation model to tell which of the customers are at highest risk of leaving. Valuable to any customer-oriented organisations. Geographic Segmentation Modeling can be applied to millions of scenarios, very valuable. (doesn't have to be for banks, churn rate, etc.). Same scenario works for (e.g. should this person get a loan or not? Should this be approved for credit => binary outcome, model, more likely to be reliable). Fradulant transactions (which is more likely to be fradulant) Binary outcome with lots of independent variables you can build a proper robust model to tell you which factors influence the outcome. alt text Problem: Classification problem with lots of independent variables (credit score, balance, number of products) and based on these variables we're predicting which of these customers will leave the bank. Artificial Neural Networks can do a terrific job with Classification problems and making those kind of predictions. Libraries used: Theano numerical computation library, very efficient for fast numerical computations based on Numpy syntax GPU is much more powerful than CPU, as there are many more cores and run more floating points calculations per second GPU is much more specialized for highly intensive computing tasks and parallel computations, exactly for the case for neural networks When we're forward propogating the activations of the different neurons in the neural network thanks to the activation function well that involves parallel computations When errors are backpropagated to the neural networks that again involves parallel computation GPU is a much better choice for deep neural network than CPU - simple neural networks, CPU is sufficient Created by Machine Learning group at the Univeristy of Montreal Tensorflow Another numerical computation library that runs very fast computations that can run on your CPU or GPU Google Brain, Apache 2.0 license Theano & Tensorflow are used primarily for research and development in the deep learning field Deep Learning neural network from scratch, use the above Great for inventing new deep learning neural networks, deep learning models, lots of line of code Keras Wrapper for Theano + Tensorflow Amazing library to build deep neural networks in a few lines of code Very powerful deep neural networks in few lines of code based on Theano and Tensorflow Sci-kit Learn (Machine Learning models), Keras (Deep Learning models) Installing Theano, Tensorflow in three steps with Anaconda installed: $ pip install theano $ pip install tensorflow $ pip install keras $ conda update --all

Experiments and deployment of ML models for churn prediction and customer retention strategy.

Customer churn prediction is the process of using machine learning models to identify customers who are likely to leave in the near future.

For the last few years Betfair is losing too many customers and is finding a solution to retain its customers. Aim of this project is to build a customer churn model to predict the customers who are about to get churned so that Betfair can implement different business strategies to retain those customers before they actually leave. The tools I am using for this analysis are R-studio and Tableau. Package mlr was chosen as the modeling package. The data for the purpose of prediction was provided by Betfair. After proper data exploration and visualization, important features for the customer churn prediction model was identified. The 8 different classification models were applied on the data in separate steps of configuring the learner task, making the learner, training the learner, prediction and performance evaluation. Out of the 8 different models, Random Forest was chosen as the best model. Cross-validation was done using random forest was done and obtained a mean miss classification error rate of 0.1278126. Hyper-parametric tuning of the random forest model was performed using package mlrHyperopt. There was only 0.05% improvement in the model accuracy after Hyper-parametric tuning. The model obtained is good enough to predict the customers who are about to fall in the churned customer category. Applying this model on the real-time data in Betfair can save huge money in revenue.

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.

Workshop to build, train and deploy XGBoost model for customer churn prediction on Amazon SageMaker

After Covid-19, with the help of advances in the technology online shopping have become a part of daily life and it is expected to grow more all around the world. Accordingly Customer behavior are becoming more and more complex with the passage of time. With increasing competitor in the market, Retailers tries their best to hold their customer because attracting new customers cost several times then retaining the existing customer. For this purpose, Retailer analysis their customers purchase so that they can provide better service and maximize their profit margins. In this work, EDA of e-Retail data has been performed, using RFM analysis to identify the categorical segmentation of customers and Time Series Analysis with ARIMA Model to identify trends and clustering and classification models are implemented to identify the customers who are likely to churn. Furthermore, will also analyze top factors that influence user retention.

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