bank-customer-churn

Predict the Churn rate of a bank.

Projects for Neural Networks course, Shahid Beheshti University, Fall 2020

This repo contains deep learning projects for beginners.

our goal for this project is to predict the churn probability of a customer using machine learning classification techniques.

In this project, I aim to predict customer churn for Deutsche Bank using supervised machine learning. It involves data exploration, feature engineering, and building Naive Bayes, Decision Tree, Random Forest, and XGBoost models. Models are tuned, evaluated, and compared to identify the best approach for churn prediction.

Detailed solution to the Ban_customer_churn_dataset from kaggle with data visualization by using Random Forest Algorithm.

Bank customer churn prediction using machine learning with cross-validation and XGBoost

Bank Customer Chun Rate

💸Bank Customer Churn Analysis & Prediction

From a dataset provided by a leading commercial bank in Vietnam, profile customers of the bank and predict who are likely to churn.

This project involves the development of a machine learning model to predict customer churn for a banking institution. By utilizing historical customer data, the model aims to identify at-risk customers, enabling the bank to take proactive measures to retain them and improve customer loyalty.

Predicting Bank Credit Card Customer Churn using the Credit Card Customers dataset.

Predictive Analysis of Customer Churn in Banking Industry Using Python

A modern Machine Learning web application that predicts customer churn and provides an interactive analytics dashboard for exploring customer behavior and business insights

Machine learning project predicting bank customer churn with a mathematically rigorous and structured workflow.