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

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.

cohort retention analysis using MySQL for online retail dataset

HR Employee Attrition Analysis

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.

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.

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.

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

Telecom Customers Churn Prediction using machine Learning Algorithm by Mohd Arman

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.

PostgreSQL-only analytics: schema + DAU, funnel, retention SQL + sample data.

사이드 프로젝트로 진행한 음식 배달 앱 로그 데이터 분석 프로젝트입니다.

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.

Customer Retention and Lifetime Value Analysis Using Cohort Analysis on AdventureWorks Dataset (2011-2014)

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

Statistical Analysis of Player Retention: An A/B Testing Study on Engagement Strategies for New and Experienced Players using the Cookie Cats Dataset