https://github.com/tulsibasetti/customer-churn-prediction-using-ann
Churn Prediction Using Neural Networks
https://github.com/tulsibasetti/customer-churn-prediction-using-ann
artificial-neural-networks classification customer-churn-prediction deep-learning keras machine-learning python3 tensorflow
Last synced: 4 months ago
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Churn Prediction Using Neural Networks
- Host: GitHub
- URL: https://github.com/tulsibasetti/customer-churn-prediction-using-ann
- Owner: TulsiBasetti
- Created: 2025-05-21T13:11:22.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-05-24T10:11:33.000Z (5 months ago)
- Last Synced: 2025-05-24T11:20:10.963Z (5 months ago)
- Topics: artificial-neural-networks, classification, customer-churn-prediction, deep-learning, keras, machine-learning, python3, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 306 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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](https://github.com/TulsiBasetti/customer-churn-prediction-using-ANN/blob/main/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:
```bash
git clone https://github.com/TulsiBasetti/customer-churn-prediction-using-ANN.git
2. Install dependencies:
```bash
pip install -r requirements.txt
3. Run the Jupyter notebook:
```bash
jupyter notebook customer-churn-prediction.ipynb