https://github.com/mohammedsaim-quadri/ann-classification-churn
Predict customer churn using a deep learning ANN model deployed with Streamlit. Interactive UI, real-time predictions, and model trained on a real-world banking dataset.
https://github.com/mohammedsaim-quadri/ann-classification-churn
ann churn-prediction classification deployment machine-learning streamlit-webapp ui
Last synced: 4 days ago
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Predict customer churn using a deep learning ANN model deployed with Streamlit. Interactive UI, real-time predictions, and model trained on a real-world banking dataset.
- Host: GitHub
- URL: https://github.com/mohammedsaim-quadri/ann-classification-churn
- Owner: MohammedSaim-Quadri
- License: gpl-3.0
- Created: 2025-04-29T19:10:13.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-29T19:52:29.000Z (about 1 year ago)
- Last Synced: 2025-08-30T04:11:14.337Z (10 months ago)
- Topics: ann, churn-prediction, classification, deployment, machine-learning, streamlit-webapp, ui
- Language: Jupyter Notebook
- Homepage: https://ann-classification-churn-3kq5m6c96mw9lz48bszpqv.streamlit.app/
- Size: 408 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ANN-Based Customer Churn Classification
A Streamlit web application that predicts the likelihood of a customer churning using an Artificial Neural Network (ANN) model trained on the **Churn Modelling Dataset**. The app enables interactive input of customer features to instantly obtain predictions based on a pre-trained deep learning model.
🔗 **Live App**: [Visit the Streamlit App](https://ann-classification-churn-3kq5m6c96mw9lz48bszpqv.streamlit.app/)
---
## Project Overview
This project addresses the business-critical problem of **customer churn prediction**. It uses a fully connected ANN trained on a dataset of bank customers. The app allows users to enter various customer attributes like age, credit score, and account balance, and outputs the probability of churn in real time.
---
## Model Details
- **Architecture**: Fully Connected ANN with multiple Dense layers
- **Framework**: TensorFlow / Keras
- **Objective**: Binary classification (Churn or Not Churn)
- **Preprocessing**:
- Label Encoding for Gender
- One-Hot Encoding for Geography
- Standard Scaling for numerical features
---
## Features
- Real-time prediction via interactive UI
- Trained deep learning model (`.h5` file)
- Cleaned and preprocessed dataset (`Churn_Modelling.csv`)
- Pickled encoders and scaler for deployment
- Streamlit frontend with sliders, input fields, and dropdowns
---
## Repository Structure
```bash
ann-classification-churn/
│ ├── app.py # Streamlit app script
├── churn_model.h5 # Trained ANN model
├── Churn_Modelling.csv # Dataset used for training
├── experiments.ipynb # Model building and tuning experiments
├── prediction.ipynb # Final prediction pipeline notebook
├── label_encoder_gender.pkl # Label Encoder for Gender
├── ohe_geo.pkl # One-Hot Encoder for Geography
├── scaler.pkl # Standard Scaler
├── requirements.txt # Python dependencies
├── LICENSE # License file
└── README.md # You're here!
```
---
## How to Run Locally
### 1. Clone the Repo
```bash
git clone https://github.com/your-username/mohammedsaim-quadri-ann-classification-churn.git
cd mohammedsaim-quadri-ann-classification-churn
```
### 2.Set Up Environment
Recommend using a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # or venv\Scripts\activate on Windows
```
### 3.Install Dependencies
```bash
pip install -r requirements.txt
```
### 4. Run the App
```bash
streamlit run app.py
```
---
## Dataset
The model is trained on the Churn Modelling Dataset which includes:
- Demographic and financial details of 10,000+ bank customers
- Target: Exited (1 = churned, 0 = retained)
Features used:
- Credit Score
- Geography
- Gender
- Age
- Tenure
- Balance
- Num of Products
- Has Credit Card
- Is Active Member
- Estimated Salary
---
## Demo Screenshot
---
## Deployment
The model is deployed using Streamlit Community Cloud with all encoders and scalers serialized via pickle.
🔗 [Visit the Streamlit App](https://ann-classification-churn-3kq5m6c96mw9lz48bszpqv.streamlit.app/)
---
## License
This project is licensed under the terms of the GNU License. See the LICENSE file for details.
---
👨💻 Author
Mohammed Saim Ahmed Quadri
📫 [LinkedIn](https://www.linkedin.com/in/msaquadri)
📧 mohammedsaimquadri@gmail.com