An explanation-first HR analytics system that reconstructs why employee exit becomes rational. Instead of predicting attrition, it generates human-readable exit narratives by decomposing pressure and retention forces, adding peer context and counterfactual interventions to reveal how stability erodes over time.

BG/NBD and Gamma Gamma probabilistic models to evaluate and predict customer churn, retention, and lifetime value of an e-commerce business

Telecom Customer Churn Analysis & Prediction project uses Gradient Boosting for precise predictions, Power BI for churn pattern visualizations, and Streamlit for interactive insights. With robust code and meticulous data preprocessing, stakeholders access accurate predictions to optimize retention and drive profitability.

Open-source warehouse-native product analytics for DuckDB, Snowflake, ClickHouse, Databricks, and Postgres. Funnels, retention, paths — directly on your warehouse, no ingestion pipeline, no per-event fees.

A predictive model for player retention/churn on day-14 after game installation based on features such as in-game metrics, user behavior, and engagement patterns to identify players at risk of churning, accurately predicting 65% of all retention within the top 6% of total population.

Investigating player retention using SQL and BigQuery

Built a SaaS analytics project using SQL and Python where I reconstructed customer lifecycle from snapshot data and analyzed key metrics like MRR, churn, cohort retention, ARPU, and LTV. I found that revenue growth was driven by customer acquisition rather than pricing, as ARPU declined over time while MRR increased

A Python package for calculating and visualizing cohort retention and other cohort-based metrics with interactive Plotly charts.

Built a customer segmentation model for SBI Life Insurance using K-Means, Hierarchical, and DBSCAN clustering, applying data preprocessing and evaluation techniques to provide tailored product recommendations and boost revenue.

This is a simple project that aims to create a basic Artificial Neural Network to predict if bank customers are going to maintain/close their accounts.

Extract data from Excel report to convert to a Power BI data model using industry best practices to create a demo replacement customer retention report.

Prediction engine that can try and understand the employment pattern for a given employee and give some interesting insights

Telecom Customers Churn Prediction using machine Learning Algorithm by Mohd Arman

Built a machine learning classification model to predict customer churn using telecom customer data.

Gaming, CRM, Fintech ve WFM gibi 6 farklı sektörde; Retention, Churn, Whale Analysis ve RFM Segmentasyonu gibi kritik iş problemlerini SQL ile çözümlediğim kapsamlı bir portfolyo çalışmasıdır.

A/B test analysis on product feature adoption — Python (scipy/pandas) + Power BI dashboard. Identified 23.7% lift in Day-7 retention for new users (p < 0.05). Ship/iterate decision memo included.

HR Employee Attrition Analysis

The Bank Customer Churn Model is a predictive analytics solution using a high-accuracy Random Forest model to identify high-risk customers, enabling banks to proactively retain valuable customers, minimize revenue loss, and inform targeted retention initiatives through user-friendly streamlit web application. User can access churn risk probability.

cohort retention analysis using MySQL for online retail dataset

This project aims to analyze sales agent performance, retailer engagement, and order conversion patterns by leveraging visit and order data. The goal is to identify key insights that can optimize sales operations, improve agent productivity, and enhance retailer retention.