---
title: Customer Churn
emoji: 🚀
colorFrom: purple
colorTo: gray
sdk: streamlit
sdk_version: 1.41.1
app_file: app.py
pinned: false
license: mit
short_description: Demo of HF Model and Dataset
---

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

MarkDown
# Customer Churn Prediction App 📊
=====================================================

## Introduction 🤔
---------------

Customer churn refers to the loss of customers or subscribers to a business or service. It's a critical issue for companies, as acquiring new customers can be costly. Predicting customer churn enables businesses to take proactive measures to retain their customers.

This app uses a machine learning model to predict customer churn based on various factors, including tenure, monthly charges, and contract type. The model is trained on a dataset from Hugging Face, and the app is deployed using Streamlit.

## Algorithm 🤖
--------------

The app uses a logistic regression model to predict customer churn. The model takes in a set of input features, including:

*   Tenure (months)
*   Monthly charges
*   Total charges
*   Contract type (month-to-month, one year, or two year)
*   Internet service type (DSL, fiber optic, or no)

The model outputs a probability of churn, which is then used to classify the customer as likely to churn or stay.

## Features of Hugging Face 🤗
--------------------------------

This app showcases the power of Hugging Face for building machine learning applications. Some of the key features of Hugging Face used in this app include:

*   **Datasets:** Hugging Face provides a wide range of datasets that can be easily accessed and shared.
*   **Models:** Hugging Face allows users to download and use pre-trained models or upload their own models.
*   **Spaces:** Hugging Face provides a simple way to deploy machine learning models as web apps.

## Value for Computer Science Students 📚
---------------------------------------------

This app provides a valuable example for computer science students looking to build machine learning applications. By exploring this app, students can learn about:

*   The importance of customer churn prediction in business
*   The use of logistic regression for classification tasks
*   The features and benefits of Hugging Face for machine learning applications
*   How to deploy machine learning models as web apps using Streamlit

## Getting Started 🚀
-------------------

To get started with this app, simply click the "Predict Churn" button and enter the required input features. The app will then output a probability of churn and classify the customer as likely to churn or stay.

### Example Use Cases 📊
-------------------------

*   **Customer Retention:** Use this app to identify customers who are likely to churn and take proactive measures to retain them.
*   **Marketing Campaigns:** Use this app to target customers who are likely to churn with personalized marketing campaigns.
*   **Resource Allocation:** Use this app to allocate resources more effectively by identifying customers who are likely to churn and require additional support.

### Future Development 🚀
-------------------------

*   **Model Improvement:** Continuously collect new data and retrain the model to improve its accuracy and robustness.
*   **Feature Engineering:** Explore new features that can be used to improve the accuracy of the model.
*   **Deployment:** Deploy the app in a production environment and monitor its performance.