-
Updated
May 18, 2024 - Jupyter Notebook
retention-analysis
Here are 25 public repositories matching this topic...
Figuring Out Which Employees May Quit
-
Updated
Sep 1, 2020 - Jupyter Notebook
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.
-
Updated
Mar 23, 2024 - Jupyter Notebook
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.
-
Updated
Dec 25, 2022
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.
-
Updated
Apr 23, 2024
cohort retention analysis using MySQL for online retail dataset
-
Updated
Aug 29, 2023
PORTFOLIO
-
Updated
Jul 6, 2023 - Jupyter Notebook
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.
-
Updated
May 24, 2024 - Jupyter Notebook
A/B testing impact of progression system changes on player retention / interaction. Non-parametric hypothesis testing and power transformations for non-normally distributed data.
-
Updated
Jul 6, 2021 - Jupyter Notebook
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.
-
Updated
Sep 25, 2020 - Jupyter Notebook
This is working with SQL queries from the book SQL FOR DATA ANALYSIS by Cathy Tanimura
-
Updated
Feb 29, 2024
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.
-
Updated
Dec 23, 2022 - Jupyter Notebook
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.
-
Updated
Aug 18, 2024 - Jupyter Notebook
BG/NBD and Gamma Gamma probabilistic models to evaluate and predict customer churn, retention, and lifetime value of an e-commerce business
-
Updated
Sep 16, 2023 - Jupyter Notebook
Retention analysis of weekly subscription cohorts
-
Updated
Feb 11, 2024
Investigating player retention using SQL and BigQuery
-
Updated
Dec 14, 2021
-
Updated
Jan 9, 2023 - Jupyter Notebook
Telecom Customers Churn Prediction using machine Learning Algorithm by Mohd Arman
-
Updated
Dec 2, 2023 - Jupyter Notebook
An comprehensive data analysis of a particular market and its customers.
-
Updated
Mar 4, 2024 - Jupyter Notebook
Customer Analytics in R
-
Updated
Jul 16, 2023 - R
Improve this page
Add a description, image, and links to the retention-analysis topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the retention-analysis topic, visit your repo's landing page and select "manage topics."