readme.md

metadata

title: Customer Churn Analysis Dashboard
emoji: 📊
colorFrom: purple
colorTo: indigo
sdk: streamlit
sdk_version: 1.41.1
app_file: app.py
pinned: false

Customer Churn Analysis Dashboard on Hugging Face Spaces

Welcome to the Customer Churn Analysis Dashboard, a comprehensive and interactive web-based tool designed to explore and visualize customer churn trends. This project is hosted on Hugging Face Spaces and offers a detailed examination of customer behavior with interactive plots and key insights for business decision-making.

📊 Project Overview

This dashboard provides an in-depth analysis of customer churn trends based on simulated customer data from a fictional telecom company called InfinityTel. The dashboard explores:

• Customer Trends: Understand patterns over time with various customer metrics.
• Churn Segments: Explore churn behavior by customer segments.
• Feature Importance: Identify the key drivers of churn.
• Insights & Actions: Key insights and suggested actions based on the analysis.

🎯 Key Features

• Interactive Visualizations: Users can switch between multiple plot types, including bar charts, line graphs, radar plots, and pie charts.
• Dropdown Filters: Select various metrics for analysis and customize the view.
• Data Insights: Actionable insights for churn reduction strategies.
• Streamlit Integration: Built using Streamlit for easy hosting on Hugging Face Spaces.

📂 Project Structure

📦 CustomerChurnAnalysis
├── 📁 data
│   └── processed_data.csv (1,000 rows of fictional customer data)
├── 📁 outputs
│   └── charts/ (generated visualizations)
├── 📁 app
│   └── app.py (Main dashboard script)
├── 📄 requirements.txt (Python dependencies)
├── 📄 README.md (This file)
├── 📄 huggingface_readme.md (Hugging Face specific README)

🚀 Getting Started

Running the App Locally

1. Clone the repository:

   git clone https://huggingface.co/spaces/therickglenn/CustomerChurnAnalysis
   

2. Install the required packages:

   pip install -r requirements.txt
   

3. Run the app:

   streamlit run app/app.py
   

Running on Hugging Face Spaces

The app is hosted live on Hugging Face Spaces. Simply visit the link and explore the dashboard without any setup!

📊 Sample Dataset

The dataset includes the following features:

• CustomerID
• Age
• Gender
• Income
• Service Tier
• Call Frequency
• Call Length
• Revenue Per Customer
• Tenure
• Churn Status

🛠️ Tools and Libraries

• Python 3.10
• Streamlit
• Pandas
• Plotly
• Seaborn
• Matplotlib

📈 Use Case Scenarios

• Identify key factors driving customer churn.
• Visualize customer segments with higher churn rates.
• Test marketing strategies to retain customers.

📧 Contact

For questions or contributions, please reach out through the Hugging Face community tab or submit a pull request.