Customer Churn Prediction

Overview:

This repository contains the implementation of a machine learning model designed to predict customer churn. The model is deployed as a web application, allowing users to input customer data and receive churn predictions in real-time.

Table of Contents:

• Features
• Installation
• Usage
• Dataset
• Model Information
• Screenshots
• Contributing
• Contact

Features:

• Interactive web application for real-time predictions.
• Comprehensive data preprocessing and feature engineering.
• Utilizes [Final Model Name] for churn prediction.
• Detailed visualizations to understand the factors affecting churn.

Installation:

1. Clone the Repository:

   git clone https://github.com/Rkpani05/Customer-Churn-Prediction.git
   cd Customer-Churn-Prediction]

2. Install Required Libraries:

   pip install -r requirements.txt

3. Unzip Model Files:

   unzip path_to_zipped_models.zip -d destination_folder/
   

Usage:

1. Run the Web Application:

   python app.py

2. Access the Application: Open a web browser and navigate to http://127.0.0.1:5000 to access the application.

3. Input Customer Data: Use the web interface to input customer data and click on 'Predict' to get the churn prediction.

Dataset:

The dataset used for this project can be found in the same directory. It contains various customer attributes and their churn status.

Model Information:

The project utilizes optimized_rf for predicting customer churn. The trained model, along with preprocessing utilities, are stored as .pkl files in the 'pkl files/' directory.

Screenshots:

Contributing:

Contributions are welcome!

Contact:

For any queries or feedback, please reach out to:

• Email: rk.pani2002@gmail.com