I'm new to machine learning and built this churn prediction model using an Artificial Neural Network (ANN) as a learning project. The goal was to understand how deep learning can be applied to classification tasks like predicting customer churn. I used TensorFlow/Keras for the ANN for data preprocessing. Feedback and suggestions are welcome! 🚀

Developed an ANN model with 92% accuracy using Keras/TensorFlow, leveraging SMOTE to improve recall by 7% over logistic regression for high-risk customer identification. Deployed as a low-latency Flask API, integrated with business dashboards for real-time churn prediction and targeted retention strategies.

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a series of algorithms that endeavors to recognize underlyinCustomer Churn prediction means knowing which customers are likely to leave or unsubscribe from your service. For many companies, this is an important prediction. This is because acquiring new customers often costs more than retaining existing ones.

A Customer Churn Prediction based on- Neural Networks, Python – Predicted customer churn for telecom providers using ANN models. – Trained neural networks by adapting weights on historical churn data.

Customer churn prediction model using artificial neural network or ANN. Use precision , recall and accuracy of this model by using confusion matrix and classification report.

Customer Churn Prediction Using ANN This project utilizes Artificial Neural Networks (ANNs) to predict customer churn. By analyzing customer data, including demographics and service usage, the model aims to identify customers at risk of leaving a company.

Customer churn prediction helps businesses identify which customers are likely to leave. Using Artificial Neural Networks (ANN), a machine learning technique, we can predict churn by analyzing patterns in customer data. This project shows how to implement an ANN model to predict customer churn and improve business retention strategies.

ANN-based customer churn prediction model using TensorFlow/Keras. Identifies at-risk customers by analyzing demographic and behavioral data, enabling proactive and data-driven retention strategies.

The following repository demonstrates the ANN classification by using various machine learning Python libraries to build a churn prediction and deployed onto the streamline web application

This project is about the Customer Churn prediction of the bank. The churn prediction process is done by using the Neural Networks and ML models. The models such as ANN-MLP, Linear Regression, Random Forest and XGBoost are used in project work.

This is a jupyter notebook for the Customer Churn predictions using Deep Learning (ANN) , it's a commun topic which can be used by the large Stores of the Banks

This is an ANN Model. WE will use telecom customer churn dataset from kaggle (link below) and build a deep learning model for churn prediction. We will also understand precision,recalll and accuracy of this model by using confusion matrix and classification report

American Express user exit prediction using an Artificial Neural Network (ANN) involves analyzing customer behavior to predict churn. By training an ANN on transaction data, spending patterns, and engagement metrics, the model identifies users likely to leave. This helps in proactive retention strategies, improving customer satisfaction

Telecom Churn Prediction uses machine learning to identify customers likely to discontinue telecom services. By analyzing customer data and applying models like Random Forest, SVM, and ANN, the project achieves 81% accuracy and helps companies reduce churn through data-driven retention strategies.

It utilizes neural network architectures to predict customer churn by analyzing factors such as customer behavior, demographics, and usage patterns. It involves training the ANN on historical customer data and using it to make accurate predictions to identify customers at risk of churning, enabling proactive retention strategies.

Telephone Customer Churn Prediction uses Artificial Neural Networks (ANN) to predict which customers are likely to leave a telecom service. By analyzing customer data, the model identifies patterns that help businesses improve retention strategie

This project focuses on building an Artificial Neural Network (ANN) for customer churn classification and deploying the model using Streamlit for a user-friendly interface. Customer churn prediction is a vital task for businesses to identify and retain at-risk customers by analyzing their demographic and behavioral data.

Customer Churn Prediction using ANN identifies customers likely to leave a service by analyzing historical data such as demographics, account details, and usage behavior. An Artificial Neural Network is trained using encoded and scaled features to model complex patterns, improve customer retention strategies.

The Bank Customer Churn Prediction app uses deep learning to predict if a bank customer will churn (leave) based on demographic and account-related data. Powered by a deep learning ANN model with TensorFlow and built with Streamlit for the front-end, this app provides an interactive interface to predict customer churn in real-time.

The Bank Customer Churn Prediction app uses deep learning to predict if a bank customer will churn (leave) based on demographic and account-related data. Powered by a deep learning ANN model with TensorFlow and built with Streamlit for the front-end, this app provides an interactive interface to predict customer churn in real-time.

This Customer Churn Prediction App uses a trained Artificial Neural Network (ANN) to predict whether a customer is likely to leave a service. By entering customer details such as demographics, service usage, and billing information, the model estimates churn probability, helping businesses make informed decisions to improve customer retention.

Churn Prediction in Telecoms Industry Using R Telecommunication market is expanding day by day. Companies are facing a severe loss of revenue due to increasing competition hence the loss of customers. They are trying to find the reasons of losing customers by measuring customer loyalty to regain the lost customers. The customers leaving the current company and moving to another telecom company are called churn. This Analysis will use ANN and SVM models to find the best model for the study.