https://github.com/shaileegavnekar/customer_churn_prediction_using_artificial_neural_networks

This project implements an Artificial Neural Network (ANN) using TensorFlow to predict customer churn based on bank customer data. It includes data preprocessing, model training, and evaluation, achieving strong accuracy in churn prediction.
https://github.com/shaileegavnekar/customer_churn_prediction_using_artificial_neural_networks

Last synced: about 2 months ago
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This project implements an Artificial Neural Network (ANN) using TensorFlow to predict customer churn based on bank customer data. It includes data preprocessing, model training, and evaluation, achieving strong accuracy in churn prediction.

• Host: GitHub
• URL: https://github.com/shaileegavnekar/customer_churn_prediction_using_artificial_neural_networks
• Owner: ShaileeGavnekar
• Created: 2024-09-11T21:14:21.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-11T22:41:00.000Z (4 months ago)
• Last Synced: 2024-09-13T09:54:06.960Z (4 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 11.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
This project builds an Artificial Neural Network (ANN) using TensorFlow to predict bank customer churn. The dataset includes features like geography, credit score, age, and salary. Key steps involve encoding categorical data, splitting the dataset, and scaling features. The ANN has two hidden layers and is trained over 100 epochs, with model performance evaluated through a confusion matrix and test set predictions.