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.

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.

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

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

HR Employee Attrition Analysis

cohort retention analysis using MySQL for online retail dataset

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.

Telco customer churn prediction with logistic regression and business insights

RFM-based customer segmentation analysis for an e-commerce dataset. Includes data cleaning, exploratory analysis, Recency-Frequency-Monetary scoring, segment classification, visual dashboards, and strategic business insights. Designed to identify high-value customers and guide targeted marketing actions

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.

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

A revenue and retention analysis for InlineTech using SQL, Excel, and Power BI, uncovering how product mix concentration, low repeat purchases, and over-reliance on direct channels stalled growth after the pandemic spike.

Telecom Customers Churn Prediction using machine Learning Algorithm by Mohd Arman

Complete Customer Retention Cohort Analysis using SQL (PostgreSQL), Power BI, Python & DAX

Cookie Cats is a hugely popular mobile puzzle game developed by Tactile Entertainment. In this project, we will look at the impact of a in-game feature change on player retention.

Product analytics A/B testing project using SQL and Python

This project analyzes sales and customer data for Contoso, a fictional e-commerce company by Microsoft.