retention-analysis
There are 1 repository under retention-analysis topic.
- Telecom-Customer-Churn-Analysis-Prediction
VibolvatanakPOCH / Telecom-Customer-Churn-Analysis-Prediction
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.
Language:Jupyter Notebook 5 a-memme / Customer_Churn_and_CLTV
BG/NBD and Gamma Gamma probabilistic models to evaluate and predict customer churn, retention, and lifetime value of an e-commerce business
Language:Jupyter Notebook 4kevin-titi / Game_Analytics_D14_Retention_Prediction
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.
Language:Jupyter Notebook 2slaing77 / Player-Retention-Analysis
Investigating player retention using SQL and BigQuery
ahmedlrashed / customer-retention-dax-report
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.
fadhlurrahmann / cohort-analysis-online-retail
cohort retention analysis using MySQL for online retail dataset
MohdArman123 / Telecom_Retention_Machine_Learning
Telecom Customers Churn Prediction using machine Learning Algorithm by Mohd Arman
Language:Jupyter Notebook 1anastasiiastepanyan / data_analytics_projects
PORTFOLIO
Language:Jupyter Notebook 0eiliaJafari / Comprehensive-market-data-analysis
An comprehensive data analysis of a particular market and its customers.
Language:Jupyter Notebook 0jacobappia / Customer-Retention-Analysis
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.
Language:Jupyter Notebook 0maxleungtszchun / customerAnalytics
Customer Analytics in R
Language:R 0merrillm1 / Ultimate_Challenge
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.
Language:Jupyter Notebook 0parkernisbet / game-ab-testing
A/B testing impact of progression system changes on player retention / interaction. Non-parametric hypothesis testing and power transformations for non-normally distributed data.
Language:Jupyter Notebook 0rachitchikara / Exploratory-Data-Analysis-for-Ed-tech-Users
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.
Language:Jupyter Notebook 0This 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.
0sinanw / ann-bank-customer-retention
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.
Language:Jupyter Notebook 0- Language:Jupyter Notebook 0
anudeepvanjavakam1 / Mobile-Games--Cookie-Cats--A_B-Testing-with-bootstrap-analysis
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.
Language:Jupyter Notebooktnation1392 / Legislator-Cohort-Analysis
This is working with SQL queries from the book SQL FOR DATA ANALYSIS by Cathy Tanimura
TrilokiDA / Employee-Retention
Figuring Out Which Employees May Quit
Language:Jupyter NotebookVaishnokmr / RFM-MODEL
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.
