Customer Churn Prediction Using Artificial Neural Networks (ANN)

This project demonstrates the application of Artificial Neural Networks (ANNs) to predict customer churn in a banking dataset. By analyzing customer features, the model identifies customers likely to leave the bank, enabling proactive retention strategies.

🧠 Project Overview

• Objective: Predict customer churn using ANN.
• Dataset: Churn_Modelling.csv
• Model: ANN built with Keras.
• Techniques:
  • Data Preprocessing: Encoding categorical variables, feature scaling.
  • Model Architecture: Input, hidden, and output layers with ReLU and Sigmoid activations.
  • Performance Evaluation: Accuracy, confusion matrix, classification report.

📁 Repository Structure

• Churn_Modelling.csv: Dataset containing customer information.
• customer-churn-prediction.ipynb: Jupyter notebook implementing the ANN model.

🚀 How to Use

1. Clone the repository:

   git clone https://github.com/TulsiBasetti/customer-churn-prediction-using-ANN.git

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the Jupyter notebook:

   jupyter notebook customer-churn-prediction.ipynb