Figuring Out Which Employees May Quit

RFM is a customer segmentation model that identifies high-value customers based on their behavior. Machine learning can be used to analyze large datasets and develop predictive models to identify customers likely to become high-value. This enables businesses to target these customers with personalized marketing strategies for increased revenue.

Predicted rider retention for a taxi service and identified most significant factors that contributed to it. Achieved an 80% accuracy with a catboost model, which was chosen for its interpretability.

This is working with SQL queries from the book SQL FOR DATA ANALYSIS by Cathy Tanimura

In this project, we conduct a time-based cohort and retention analysis in python to examine how many customers are staying and how many are leaving in a given cohort over time.

A/B testing impact of progression system changes on player retention / interaction. Non-parametric hypothesis testing and power transformations for non-normally distributed data.

PORTFOLIO

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.

Retention analysis of weekly subscription cohorts

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.

An comprehensive data analysis of a particular market and its customers.

Customer Analytics in R

This repository contains SQL queries to calculate the retention rate for an application called Kolo. The queries are written in standard SQL and can be used with any database that supports SQL.The queries are well-documented and easy to follow. They can be used as a starting point for anyone who wants to calculate the retention rate for an app.

This repository contains Python pandas code to perform exploratory data analysis (EDA) on a dataset of users who churned and then rejoined the platform. The report includes the number of win-back users in each week, the average number of days it took for users to rejoin the platform.

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.

cohort retention analysis using MySQL for online retail dataset

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.

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.

Telecom Customers Churn Prediction using machine Learning Algorithm by Mohd Arman