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.

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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.

Predict customer churn in e-commerce retail using Python, scikit-learn, XGBoost, and PCA. Includes data preprocessing, EDA, feature engineering, and model training (Logistic Regression, Random Forest, Gradient Boosting). Hyperparameter tuning with GridSearchCV and deployment for new customer predictions.

A collection of applied AI use cases for the telecom retail industry. Includes ready-to-use demos for customer churn prediction, referral-based growth engines, customer segmentation, and more, designed to help telecom operators retain customers and drive acquisition using machine learning and predictive analytics.

Retail Customer Segmentation & Churn Prediction

The repository carries our work on the user churn prediction problem in the e-commerce retail environment and related paper.

End-to-end churn prediction case study for retail banking, combining a leakage-safe ML pipeline, model comparison, and threshold-based targeting with scenario-driven revenue impact simulation to translate predictions into actionable retention decisions.

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This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for online retail. Aggregations on the data to generate invoice based and customer based data sets are performed. Churn value is determined based on the customer’s transactions.

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End-to-End Retail Customer Churn Prediction using Gradient Boosting and Streamlit. This repository showcases a comprehensive data science workflow, from feature engineering with RFM to building a Gradient Boosting model and deploying an interactive dashboard for actionable customer retention insights.

End-to-end retail analytics pipeline: RFM segmentation, churn prediction, CLV forecasting, basket analysis & cohort retention — powered by Python, MySQL & Power BI.

Customer segmentation and churn prediction dashboard built with Streamlit. Uses KMeans clustering and XGBoost with 91.7% accuracy and 98.5% ROC AUC on real UK retail data.

This project analyzes Adidas customer data using demographics, transactions, store locations, loyalty metadata, and churn information. It delivers insights on customer behavior, segmentation, churn prediction, and retail performance through data cleaning, visualization, and machine learning to support data-driven business decisions.

AI-powered business intelligence system combining RAG, LLMs, and machine learning for retail analytics. Features customer segmentation, churn prediction, sales forecasting, and intelligent document querying. Built with LangChain, FAISS, and scikit-learn.

This is a Customer Churn Prediction project in E-Commerce Retail using Stacking based Ensemble Learning Method using Logistic Regression as Meta Learner and KNN, SVM, Decision Trees and Random forests as base learners

An end-to-end customer analytics project using the Online Retail II dataset. This work features RFM segmentation, churn prediction with XGBoost, Customer Lifetime Value (CLV) forecasting with BG/NBD & Gamma-Gamma models, and statistical A/B testing.

Customer churn prediction and retention analytics for South African retail banking. Built end-to-end ML pipeline with feature engineering, XGBoost modeling (0.86 AUC), and Power BI dashboards. Quantified R578.5M revenue at risk and delivered 1,604% ROI retention strategy with actionable risk segmentation.

End-to-end MLOps pipeline for e-commerce customer analytics. It uses the Online Retail II dataset to run RFM segmentation, churn prediction, and CLV modeling on Spark. Airflow orchestrates the workflow, MLflow tracks experiments and models, DVC versions data, and Streamlit provides an interactive UI—services are containerized with Docker.

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Predict and prevent customer churn in e-commerce. Leverage data science to boost retention and profitability.

Retail churn prediction interactive demo

Retail Analytics & Customer Churn Prediction System

Retail - Customer Segmentation & Churn Prediction Assignment

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End-to-end AI project for customer churn prediction, covering functional framing, production-ready ML implementation with MLflow, and explainable AI using SHAP

To identify customers with a probability of churn, i.e., recall should be high, but not at the cost of introducing many false positives.

ML project for retail churn detection

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