🧠 ANN Customer Churn Classification

A deep learning project that predicts whether a bank customer will churn (exit) using an Artificial Neural Network (ANN) built with TensorFlow/Keras. The model is deployed as an interactive web app using Streamlit.

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📌 Problem Statement

Customer churn is one of the biggest challenges for banks. This project builds a binary classification model to predict if a customer will leave the bank based on their profile data, enabling proactive retention strategies.

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🗂️ Project Structure

ANN-Classification/
│
├── app.py                      # Streamlit web application
├── experiments.ipynb           # Model training notebook
├── model.h5                    # Trained ANN model
├── label_encoder_gender.pkl    # Label encoder for Gender
├── onehot_encoder_geo.pkl      # One-hot encoder for Geography
├── scaler.pkl                  # Standard scaler for features
├── Churn_Modelling.csv         # Dataset
├── requirements.txt            # Python dependencies
└── logs/                       # TensorBoard training logs

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🧬 Dataset

The dataset contains 10,000 bank customer records with the following features:

Feature	Description
CreditScore	Customer credit score
Geography	Country (France, Germany, Spain)
Gender	Male / Female
Age	Customer age
Tenure	Years with the bank
Balance	Account balance
NumOfProducts	Number of bank products used
HasCrCard	Has credit card (1/0)
IsActiveMember	Active member (1/0)
EstimatedSalary	Estimated annual salary
Exited	Target: 1 = Churned, 0 = Stayed

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🏗️ Model Architecture

Input Layer  →  64 neurons (ReLU)
                    ↓
Hidden Layer →  32 neurons (ReLU)
                    ↓
Output Layer →   1 neuron (Sigmoid)

• Optimizer: Adam (learning rate = 0.01)
• Loss: Binary Crossentropy
• Metrics: Accuracy
• Callbacks: EarlyStopping, TensorBoard
• Epochs: 100

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⚙️ Installation & Setup

1. Clone the repository

git clone https://github.com/amnsingh05/ANN-Classification-Churn.git
cd ann-classification-churn

2. Create a virtual environment

python -m venv venv
venv\Scripts\activate        # Windows
source venv/bin/activate     # Mac/Linux

3. Install dependencies

pip install -r requirements.txt

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🚀 Running the App

streamlit run app.py

Then open your browser at http://localhost:8501

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📊 TensorBoard (Training Visualization)

To view training metrics:

tensorboard --logdir logs/fit

Then open http://localhost:6006

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📦 Requirements

tensorflow==2.15.0
pandas==2.2.2
numpy==1.26.4
scikit-learn==1.4.2
tensorboard==2.15.2
matplotlib==3.9.0
streamlit==1.35.0
protobuf==4.25.3

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🌐 Live Demo

👉 https://ann-classification-churn-4vr76pfj9xntbmbd5m62om.streamlit.app/

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🛠️ Tech Stack

• Python 3.11
• TensorFlow / Keras — ANN model
• Scikit-learn — Preprocessing
• Pandas / NumPy — Data manipulation
• Streamlit — Web app
• TensorBoard — Training visualization

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📄 License

This project is open source and available under the MIT License.