This project focuses on predicting customer churn for a bank's credit card users using an Artificial Neural Network (ANN). The model classifies whether a customer is likely to leave (churn) based on various behavioral and demographic features.

📄Description

🔍 Highlights:

• Built using TensorFlow and Keras

• Includes data preprocessing, EDA, and feature scaling

• Trained a binary classification ANN with relu activation

• Evaluated using accuracy, loss curves, and confusion matrix

📥Datasets

The dataset is available here..

https://www.kaggle.com/datasets/rjmanoj/credit-card-customer-churn-prediction/data

🔧Requirements

• Python
• TensorFlow & Keras
• Pandas & NumPy
• scikit-learn
• Matplotlib & Seaborn

🚀 How to Use This Repository

You can run this project directly on Google Colab without setting up anything locally:

✅ Steps:

1. Clone the repository to your system:

   git clone <repository-url>

   Replace <repository-url> with the actual link to this repo.

2. Go to Google Colab

3. Click on File > Upload notebook
   and upload the .ipynb file from the cloned folder and run the file