OroCRM - an open-source Customer Relationship Management application.
Data Science & Machine Learning Internship at Flip Robo Technologies
Source-driven decision hub inspired by the PwC Switzerland / Forage Power BI virtual case experience, deployed on GitHub Pages.
Retainful Website
Retail analytics in Python: RFM, HML and threshold segmentation, gain/loss, cross-shop, cohort retention, revenue trees and market basket analysis, on pandas or in your database via Ibis
Unified ML platform serving two production risk models behind one API, fraud detection using a Logistic Regression pipeline at 92.4 percent accuracy and 0.90 F1, and churn prediction using a five-estimator hard-voting ensemble at 85.6 percent accuracy, with SMOTE balancing, sub-0.5 second inference, and CI-enforced 90 percent test coverage.
Abandoned Cart Recovery Email and Next Order Coupon Plugin for WooCommerce. Easily recover abandoned carts with a single click and drive repeat purchases with Retainful
Loyalty Bridge is a web-based loyalty management system that enables businesses to track and incentivize customer loyalty.
Predicts which telecom customers are likely to churn with 95% accuracy using real-world data features from usage, billing, and support data. Implements Sturges-based binning, one-hot encoding, stratified 80/20 train-test split, and a two-level ensemble pipeline with soft voting. Achieves 94.60% accuracy, 0.8968 AUC, 0.8675 precision, 0.7423 recall.
ChurnShield – AI-powered Flask web app predicting customer churn and generating personalized retention strategies with a Random Forest ML pipeline and admin dashboard.
This project predicts Customer Lifetime Value (CLV) for e-commerce. It aims at forecasting the revenue a business can expect from a customer over time. I did an explatory analysis. From Linear Regression to Neural Networks, explore how different models perform in predicting CLV.
This demo repository demonstrates how to analyze customer reviews with Azure OpenAI Service (AOAI). I leveraged "ASOS Customer Review" from Kaggle to obtain valuable insight from the customer review content.
Customer churn analysis project using Excel and Power BI. This project investigates the exit patterns of banking customers using various demographics and behavior indicators such as age, gender, credit card status, geography, and credit score. Insights help identify key drivers of churn and guide retention strategies.
Assessed brand loyalty patterns and price elasticity metrics to provide insights for brand’s market growth. Recommended strategies for the top 3 brands based on competitor analysis, customer profiling and customer retention through RFM analysis and multinomial logit.
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.
Lifecycle marketing & CRM engine for Claude Code — scores your data first, then generates customer journeys (onboarding, retention, churn, win-back) with channel-ready copy
Customer churn prediction system using XGBoost, SHAP explainability, and Streamlit for real-time telecom retention analysis.
Lifecycle, CRM and retention marketing skills for AI agents: email, SMS, loyalty, win-back, segmentation. 35 Agent Skills with the steps, thresholds, timing, edge cases and failure modes a framework leaves to you. Checked in Claude Code; Codex, Cursor and other agents that read SKILL.md load the same files.
The Bank Churn Classification project predicts customer churn in the banking sector using machine learning algorithms and EDA. It features a user-friendly interface built with HTML and CSS, with model deployment via Flask. This helps banks identify churn patterns and implement strategies to retain customers.
To associate your repository with the customer-retention topic, visit your repo's landing page and select "manage topics."