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+# 🏦 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.
+