Determining the churn rate of a bank and predicting which of their customers are at high risk of leaving the bank.
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Updated
Mar 28, 2019 - Python
Determining the churn rate of a bank and predicting which of their customers are at high risk of leaving the bank.
This project involves performing customer segmentation and RFM (Recency, Frequency, Monetary) analysis on customer data from a retail company. The primary goal is to categorize customers into segments based on their buying behavior and identify potential target groups for marketing campaigns.
The effect of social interaction on individual churn decision in MMORPG Game
This project aims to perform customer segmentation and revenue prediction for a gaming company based on customer attributes. The company wants to create persona-based customer definitions and segment customers based on these personas to estimate how much potential customers can generate in revenue.
"ChurnMaster is an advanced machine learning tool designed to predict customer churn by analyzing behavioral patterns and usage data to help businesses enhance customer retention strategies.
It is focused on customer churn utilizing the personal info and various user statistics on a telecom user database from U.S. The performance of the final model is 98.8% accuracy and 96.4% f-beta score so far.
In this project, you will implement your learnings to identify credit card customers that are most likely to churn. The completed project will include a Python package for a machine learning project that follows coding (PEP8) and engineering best practices for implementing software (modular, documented, and tested). The package will also have th…
An artificial neural network predicting whether or not a customer will leave the bank or not based on certain independent factors using the Churn Modelling Dataset
Despliegue de un modelo de Random forest en streamlit para predecir los clientes que son propensos a abandonar la compañia.
The goal of this project is to develop a predictive model that identifies customers who are at risk of churning from a business
A dynamic risk assessment system in which a customer churn model is monitored after deployment.
Churn ML Model Deployment with FastApi and Docker
Data Science Machine Learning Repositories
A comprehensive analysis on an online retail transaction data set for 13 months.
Developed models to predict customer churn probability and analyze factors that influence user retention
A Streamlit app to predict Telecom Churn
Predicts weather the customer will stay with the current telecom company or leave
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