Bank Customer Churn Prediction using Artificial Neural Networks (ANN)

Introduction

Customer churn, the phenomenon where customers discontinue their relationship with a business, is a significant concern in the banking industry. Predicting customer churn can help banks take proactive measures to retain valuable customers. This project aims to develop a predictive model using Artificial Neural Networks (ANN), a type of deep learning algorithm, to identify customers who are likely to churn.

Dataset

The dataset used for training and evaluation consists of customer information obtained from a bank. It includes various attributes such as age, gender, account balance, transaction history, etc., along with a binary label indicating whether the customer has churned or not. The dataset is not included in this repository due to its size, but a sample dataset or your own dataset can be used to replicate the project.

Methodology

The project follows the following steps to predict customer churn:

1. Data Preprocessing: The raw dataset is preprocessed to handle missing values, categorical variables, and feature scaling. Data is divided into training and testing sets.

2. Model Architecture: An ANN model is constructed using TensorFlow. The architecture includes an input layer, one or more hidden layers, and an output layer. Activation functions, dropout, and batch normalization techniques may be employed to enhance model performance.

3. Model Training: The ANN model is trained using the preprocessed training dataset. The training process involves forward propagation, backpropagation, and weight optimization using gradient descent algorithms. The model is iteratively trained on the data to minimize the prediction error.

4. Model Evaluation: The trained model is evaluated using the preprocessed testing dataset. Evaluation metrics such as accuracy, precision, recall, and F1-score are calculated to assess the model's performance in predicting customer churn.

5. Prediction: The trained model can be used to make predictions on new, unseen customer data. The input data needs to be preprocessed in the same manner as the training data before being fed into the model.

Conclusion

The Bank Customer Churn Prediction project demonstrates the application of Artificial Neural Networks (ANN) for predicting customer churn in the banking industry. By training an ANN model on customer data, banks can proactively identify customers who are likely to churn and take appropriate measures to retain them. The project provides a foundation for further research and development in customer churn prediction using deep learning techniques.