Customer Churn Prediction

This project is a Flask-based web application for predicting customer churn using a machine learning model. It allows users to input customer data and receive predictions on whether a customer is likely to churn.

Features

• Web interface for customer data input
• Machine learning model for churn prediction
• Configurable model and preprocessor paths
• Easy deployment and local development

Project Structure

.
├── app.py
├── artifacts/
│   ├── model.pkl
│   ├── preprocessor.pkl
│   └── top_features.json
├── requirements.txt
├── .env
├── .gitignore
└── README.md

Getting Started

Prerequisites

• Python 3.7+
• pip

Installation

1. Clone the repository:

   git clone https://github.com/004Gaurav/Customer_Churn.git
   cd Customer_Churn

2. Create a virtual environment and activate it:

   python -m venv venv
   venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Configure environment variables:

   • Edit the .env file as needed.

5. Add model artifacts:

   • Place your model.pkl, preprocessor.pkl, and top_features.json in the artifacts/ directory.

Running the App

flask run

The app will be available at http://localhost:5000.

Configuration

• All configuration is managed via the .env file.
• Model and preprocessor paths can be changed as needed.

License

This project is licensed under the MIT License.