Bank customers churn dashboard with predictions from several machine learning models.

This repository will have all the necessary files for machine learning and deep learning based Banking Churn Prediction ANN model which will analyze tha probablity for a customer to leave the bank services in near future. Deployed on Heroku.

Predict the Churn rate of a bank.

Bank customer churn prediction using multiple ml models

Built an artificial Neural Network from scratch to help the operations team to identify the customers that are more likely to churn.

Bank Customer Churn Prediction is a machine learning project aimed at predicting whether a bank customer is likely to leave (churn) or stay with the bank. The dataset typically contains customer information such as credit score, age, balance, estimated salary, tenure, number of products, credit card ownership, activity status, gender, and country.

Business Problem: Dataset of a bank with 10,000 customers measured lots of attributes of the customer and is seeing unusual churn rates at a high rate. Want to understand what the problem is, address the problem, and give them insights. 10,000 is a sample, millions of customer across Europe. Took a sample of 10,000 measured six months ago lots of factors (name, credit score, grography, age, tenure, balance, numOfProducts, credit card, active member, estimated salary, exited, etc.). For these 10,000 randomly selected customers and track which stayed or left. Goal: create a geographic segmentation model to tell which of the customers are at highest risk of leaving. Valuable to any customer-oriented organisations. Geographic Segmentation Modeling can be applied to millions of scenarios, very valuable. (doesn't have to be for banks, churn rate, etc.). Same scenario works for (e.g. should this person get a loan or not? Should this be approved for credit => binary outcome, model, more likely to be reliable). Fradulant transactions (which is more likely to be fradulant) Binary outcome with lots of independent variables you can build a proper robust model to tell you which factors influence the outcome. alt text Problem: Classification problem with lots of independent variables (credit score, balance, number of products) and based on these variables we're predicting which of these customers will leave the bank. Artificial Neural Networks can do a terrific job with Classification problems and making those kind of predictions. Libraries used: Theano numerical computation library, very efficient for fast numerical computations based on Numpy syntax GPU is much more powerful than CPU, as there are many more cores and run more floating points calculations per second GPU is much more specialized for highly intensive computing tasks and parallel computations, exactly for the case for neural networks When we're forward propogating the activations of the different neurons in the neural network thanks to the activation function well that involves parallel computations When errors are backpropagated to the neural networks that again involves parallel computation GPU is a much better choice for deep neural network than CPU - simple neural networks, CPU is sufficient Created by Machine Learning group at the Univeristy of Montreal Tensorflow Another numerical computation library that runs very fast computations that can run on your CPU or GPU Google Brain, Apache 2.0 license Theano & Tensorflow are used primarily for research and development in the deep learning field Deep Learning neural network from scratch, use the above Great for inventing new deep learning neural networks, deep learning models, lots of line of code Keras Wrapper for Theano + Tensorflow Amazing library to build deep neural networks in a few lines of code Very powerful deep neural networks in few lines of code based on Theano and Tensorflow Sci-kit Learn (Machine Learning models), Keras (Deep Learning models) Installing Theano, Tensorflow in three steps with Anaconda installed: $ pip install theano $ pip install tensorflow $ pip install keras $ conda update --all

Bank Credit Card Churn Prediction ML Project

Customer churn prediction with feature engineering and LightGBM optimization.

Final Project JCDS07 BSD | Andra P Utama | Bank Customer Churn Prediction

This repository will have all the necessary files for machine learning and deep learning based Banking Churn Prediction ANN model which will analyze tha probablity for a customer to leave the bank services in near future.

Customer Clustering and Churn Prediction in a Bank.

This repository contains a comprehensive solution for predicting bank customer churn using machine learning techniques.

Bank Customer Churn Prediction - Exploratory Data Analysis

To analyze bank customer churn behavior, identify key attrition factors, and develop predictive models to forecast churn probabilities, aiding proactive customer retention strategies using advanced data analysis and machine learning techniques.

A Bank Customer Churn Prediction project utilizing Machine Learning models to analyze customer data, predict churn behavior, and provide actionable insights to improve customer retention strategies.

This project deals with the classification of the bank customers on whether a customer will leave the bank (i.e.; churn) or not, by applying the below steps of a Data Science Project Life-Cycle 1. Data Exploration, Analysis and Visualisations 2. Data Pre-processing 3. Data Preparation for the Modelling 4. Model Training 5. Model Validation 6. Optimized Model Selection based on Various Performance Metrics 7. Deploying the Best Optimized Model into Unseen Test Data 8. Evaluating the Optimized Model’s Performance Metrics The business case of determining the churn status of bank customers are explored, trained and validated on 7 different classification algorithms/models as listed below and the best optimized model is selected based on the accuracy metrics. 1. Decision Tree Classifier - CART (Classification and Regression Tree) Algorithm 2. Decision Tree Classifier - IDE (Iterative Dichotomiser) Algorithm 3. Ensemble Random Forest Classifier Algorithm 4. Ensemble Adaptive Boosting Classifier Algorithm 5. Ensemble Hist Gradient Boosting Classifier Algorithm 6. Ensemble Extreme Gradient Boosting (XGBoost) Classifier Algorithm 7. Support Vector Machine (SVM) Classifier Algorithm

This repo contains a machine learning project for predicting bank customer churn using XGBoost. It includes data preprocessing, model training, and deployment as an API and Streamlit app in a Docker container. The repo also contains a report and images documenting the project.

Machine learning project to predict bank customer churn using classification models.

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The Aim of this project is to identify customers likely to churn their savings account balances below the minimum balance using the customer’s data like their demographic information, Customer-Bank Relationship and Transactional Information. The data set for this project is downloaded from Kaggle.

Churn Modelling with Bank Customer Prediction using ANN: Utilizing Artificial Neural Networks for predicting customer churn in banking scenarios.

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Bank customer churn prediction using logistic regression

End-to-end machine learning project to predict bank customer churn using behavioral, demographic, and transaction data.

This project aims to develop an accurate customer churn prediction system for the banking industry to proactively retain customers and mitigate revenue loss.

This project aims to predict customer churn for a bank, using demographic information, customer behavior, and whether or not the customer has churned. The dataset used in this project is from the UCI Machine Learning Repository.

An End-to-End Data Science Project: Churn Prediction for Bank Customers