Customer Churn Prediction

A machine learning-powered application for predicting customer churn using a custom-trained model. This project demonstrates the efficient use of serialized model components to enhance reusability and deployment, offering an interactive interface via Streamlit for real-time predictions.

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🚀 Live Demo

Deployed Website: Visit Here

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📹 Demo

This demonstration shows how users can input customer data and get real-time churn predictions through the interactive Streamlit app.

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Key Features

• Interactive Interface: Built using Streamlit for seamless user experience.
• Custom-trained Model: Predicts customer churn with high accuracy.
• Reusable Components: Serialized model (.h5) and preprocessing objects (.pkl files) for scalable and maintainable AI solutions.
• Modular Design: Easy integration and deployment in real-world use cases.

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Installation

1. Clone this repository:

   git clone https://github.com/username/Customer-Churn-Prediction.git
   cd Customer-Churn-Prediction
   

2. Setup a virtual environment

   python -m venv venv
   source venv/bin/activate
   

3. Install Dependencies

   pip install -r requirements.txt
   

Usage

1. Run the Application

   streamlit run app.py
   

2. Input Customer Data Use the interactive interface to input customer attributes

3. View Results Get real-time predictions on whether a customer is likely to churn.