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Churn Prediction with ANN using Streamlit Web App

This project demonstrates the implementation of a churn prediction model using Artificial Neural Networks (ANN) and deploys it as an interactive Streamlit web app for real-time predictions.

Table of Contents

• Overview
• Technologies Used
• How to Run the Project
• File Structure
• Model Explanation
• License

Overview

The churn prediction model is built using Artificial Neural Networks (ANN) to predict whether a customer will churn based on historical data. The model is integrated with a Streamlit web application to provide a user-friendly interface for making real-time predictions.

Technologies Used

• Python
• TensorFlow/Keras (for ANN)
• Streamlit (for web app)
• Pandas (for data manipulation)
• Scikit-Learn (for preprocessing and model evaluation)
• Matplotlib (for visualizations)

How to Run the Project

Prerequisites

Before running the project, ensure that you have the following installed:

• Python (version 3.6+)
• pip (Python package manager)

Installation

1. Clone this repository to your local machine:

   git clone https://github.com/your-username/churn-prediction-ann-streamlit.git
   cd churn-prediction-ann-streamlit

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # For Windows use: venv\Scripts\activate

3. Install the required dependencies:

   pip install -r requirements.txt

Running the Streamlit App

To run the churn prediction Streamlit app, use the following command:

streamlit run app.py

This will start a local server and open the Streamlit web app in your default browser.

Model Training (if needed)

If you need to train the model yourself, run the train_model.py script to train the ANN model on your dataset. The trained model will be saved as churn_model.h5.

python train_model.py

Once trained, the model will be ready for use in the Streamlit app.

File Structure

/churn-prediction-ann-streamlit
│
├── app.py              # Streamlit app file
├── train_model.py      # Script for training the ANN model
├── churn_model.h5      # Trained model (saved)
├── requirements.txt    # List of required Python packages
├── data/
│   ├── churn_data.csv  # Dataset for training the model
│
└── README.md           # Project documentation

Model Explanation

The churn prediction model is built using an Artificial Neural Network (ANN). The model is trained to predict whether a customer will churn based on various features, such as customer demographics, account details, and usage patterns. The ANN consists of input layers, hidden layers, and an output layer. We use the Keras library to implement the ANN and the TensorFlow framework for training.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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This README gives clear instructions on how to set up and run the churn prediction model with Streamlit. Let me know if you'd like any adjustments or additional details!