# 🏦 Bank Customer Churn Prediction using Artificial Neural Network (ANN)

## Overview

This project is an AI-powered **Bank Customer Churn Prediction System** developed using **Deep Learning (Artificial Neural Networks)**. The application predicts whether a customer is likely to leave the bank based on their personal and financial information.

The model is deployed using **Streamlit** and can be easily hosted on **Hugging Face Spaces** for public access.

---

## Features

* Deep Learning-based customer churn prediction
* Interactive Streamlit web interface
* Real-time predictions
* Standardized input preprocessing using StandardScaler
* Probability score for each prediction
* Clean and user-friendly interface

---

## Dataset

The project uses the **Bank Customer Churn Dataset**.

### Input Features

| Feature         | Description                              |
| --------------- | ---------------------------------------- |
| CreditScore     | Customer's credit score                  |
| Gender          | Male or Female                           |
| Age             | Customer age                             |
| Tenure          | Number of years with the bank            |
| Balance         | Current account balance                  |
| NumOfProducts   | Number of bank products owned            |
| HasCrCard       | Whether the customer has a credit card   |
| IsActiveMember  | Whether the customer is an active member |
| EstimatedSalary | Estimated annual salary                  |

### Target Variable

| Target | Meaning                 |
| ------ | ----------------------- |
| 0      | Customer Stays          |
| 1      | Customer Leaves (Churn) |

---

## Technologies Used

* Python
* TensorFlow / Keras
* NumPy
* Pandas
* Scikit-learn
* Joblib
* Streamlit

---

## Deep Learning Architecture

* Input Layer: **9 Features**
* Hidden Layer 1: Dense Layer (ReLU)
* Hidden Layer 2: Dense Layer (ReLU)
* Output Layer: Dense Layer (Sigmoid)

### Activation Functions

* ReLU
* Sigmoid

### Loss Function

Binary Crossentropy

### Optimizer

Adam Optimizer

### Evaluation Metric

Accuracy

---

## Data Preprocessing

The following preprocessing steps were applied before training:

* Removed unnecessary columns
* Encoded Gender
* Feature Scaling using StandardScaler
* Train-Test Split
* Normalized numerical features

The trained StandardScaler is saved as:

```
scaler.pkl
```

to ensure identical preprocessing during deployment.

---

## Model Files

```
ann_model.h5
```

Trained Artificial Neural Network model.

```
scaler.pkl
```

Saved StandardScaler used during training.

```
app.py
```

Streamlit application.

---

## Project Structure

```
Bank-Customer-Churn-Prediction/
│
├── app.py
├── ann_model.h5
├── scaler.pkl
├── requirements.txt
├── README.md
└── dataset.csv
```

---

## Installation

Clone the repository

```bash
git clone <repository-url>
```

Move into the project

```bash
cd Bank-Customer-Churn-Prediction
```

Create Virtual Environment

```bash
python -m venv venv
```

Activate Virtual Environment

### Windows

```bash
venv\Scripts\activate
```

### Linux / macOS

```bash
source venv/bin/activate
```

Install dependencies

```bash
pip install -r requirements.txt
```

---

## Run the Application

```bash
streamlit run app.py
```

---

## How the Prediction Works

1. User enters customer information.
2. Input data is converted into numerical format.
3. Features are scaled using the saved StandardScaler.
4. The trained ANN predicts the probability of customer churn.
5. If the probability is greater than **0.5**, the customer is predicted to leave the bank; otherwise, the customer is predicted to stay.

---

## Example Prediction

### Input

```
Credit Score: 619
Gender: Female
Age: 42
Tenure: 2
Balance: 0
Products: 1
Credit Card: Yes
Active Member: Yes
Estimated Salary: 101348.88
```

### Output

```
Prediction Probability: 0.34

Customer is likely to Stay.
```

---

## Requirements

```
streamlit
tensorflow
numpy
pandas
scikit-learn
joblib
```

---

## Future Improvements

* Support Geography feature
* Explain predictions using SHAP
* Interactive analytics dashboard
* Batch prediction from CSV
* REST API using FastAPI
* Docker deployment
* Cloud deployment on AWS or Azure

---

## Deployment

This project can be deployed on:

* Hugging Face Spaces
* Streamlit Community Cloud
* Render
* Railway
* AWS EC2
* Microsoft Azure
* Google Cloud Platform

---

## Author

**Sudheer Muthyala**

B.Tech – Electronics and Communication Engineering

Aspiring Data Scientist | AI & Machine Learning Enthusiast

---

## License

This project is intended for educational and portfolio purposes. Feel free to fork, modify, and build upon it while providing appropriate attribution.