Customer Churn Detection App

This web application is designed to predict customer churn using an Artificial Neural Network (ANN). The app is built using Streamlit, a Python library for creating interactive web applications.

Live Demo:

A live demo of the application is available at https://prajuktadey-customer-churn-prediction.streamlit.app/.

Overview:

Customer churn refers to the phenomenon of customers discontinuing their business relationship with a company. Predicting customer churn is crucial for businesses to understand customer behavior and take proactive measures to retain them.

This app provides a user-friendly interface to input customer data and predict the likelihood of churn using an Artificial Neural Network model. The app takes various customer-related features as inputs, such as customer demographics, usage patterns, and service history, and generates a churn prediction based on the trained model.

Usage:

To run the application locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/your-username/customer-churn-prediction.git

2. Install the required dependencies:

3. pip install -r requirements.txt

4. Run the Streamlit app:

   streamlit run main.py

5. Access the app in your web browser by visiting http://localhost:8501.

Project Structure

The project directory contains the following files and directories:

• main.py: The main application file that defines the Streamlit app and its user interface.
• model.sav: The pre-trained ANN model used for churn prediction.
• requirements.txt: The list of required Python packages and their versions.

Dependencies:

The following Python packages are used in this project:

• Streamlit: streamlit
• NumPy: numpy
• Pandas: pandas
• Scikit-learn: scikit-learn
• TensorFlow: tensorflow
• Keras: keras

You can find the complete list of dependencies and their versions in the requirements.txt file.