metadata

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.