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Churn Prediction Web App using Gradio

Overview

This repository hosts a web application developed with Gradio that predicts customer churn for a telecommunications company. By inputting customer information like tenure, monthly charges, and total charges, the app provides predictions about the likelihood of a customer churning.

Usage Instructions

  1. Clone the Repository:

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

  2. Install Dependencies:

    pip install gradio numpy scikit-learn

  3. Run the Gradio Interface:

    gradio src/app.py

  4. Access the App:

    Open a web browser and navigate to the URL displayed in the terminal (usually http://127.0.0.1:7860). The Churn Prediction Web App interface will be visible.

  5. Interact with the App:

    Adjust the sliders for tenure, monthly charges, and total charges to observe real-time churn predictions.

App Interface

Before Prediction

App Screenshot

After Prediction

App Screenshot

Model Details

The predictive model utilized in this app is a fine-tuned Logistic Regression model. It assesses whether a customer is likely to churn, considering input characteristics like tenure, monthly charges, and total charges. The model was trained using a processed dataset with the Scikit-learn library.

About the Project

This project serves as a demonstration of deploying a machine learning model via Gradio. It can be used as a blueprint for developing similar predictive web applications.

Author Information

Licensing

This project operates under the MIT License. To understand the terms and conditions, refer to the LICENSE file. Make sure to replace "your-username" with your actual GitHub username and provide accurate URLs for your GitHub profile and LinkedIn profile. Additionally, include the app_screenshot.png file (a screenshot of your app) in the repository, and if needed, upload the app.py file containing the Gradio code, along with any pertinent model weights or preprocessing steps.