Update README.md

README.md

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 ---
-title: Telecom Churn Dashboard
-emoji: 🚀
-colorFrom: red
+title: Customer Churn Analysis - Interactive EDA
+emoji: 📊
+colorFrom: blue
 colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
+sdk: streamlit
+sdk_version: 1.28.0
+app_file: eda_app.py
 pinned: false
-short_description: Telecom Churn Dashboard for EDA.
 license: mit
+tags:
+- data-analysis
+- customer-churn
+- telecommunications
+- streamlit
+- plotly
+- eda
+- data-visualization
+- business-intelligence
 ---
 
-# Welcome to Streamlit!
+# 📊 Customer Churn Analysis - Interactive EDA
+
+An interactive Streamlit application for comprehensive exploratory data analysis of telecommunications customer churn data. This tool provides business analysts and data scientists with powerful visualizations and insights to understand customer behavior patterns and identify churn risk factors.
+
+## 🚀 Live Demo
+
+Try the interactive application: [Customer Churn EDA](https://huggingface.co/spaces/disham993/telecom-churn-dashboard)
+
+## ✨ Features
+
+### 📋 Dataset Overview
+- **Comprehensive Data Summary**: Key statistics, data types, and sample exploration
+- **Missing Value Analysis**: Complete data quality assessment
+- **Dataset Description**: Detailed explanation of telecommunications churn data
+
+### 🎯 Churn Analysis
+- **Interactive Churn Distribution**: Pie charts and statistical breakdowns
+- **Categorical Analysis**: Churn rates by service plans and customer segments
+- **Feature Comparison**: Box plots and histograms comparing churned vs retained customers
+
+### 📍 Geographic Analysis
+- **Interactive US Choropleth Maps**: State-wise churn rate visualization
+- **Regional Patterns**: Top states by churn rate with detailed metrics
+- **Area Code Analysis**: Geographic distribution insights
+
+### 📞 Usage Patterns
+- **Time-based Analysis**: Day, evening, night, and international usage patterns
+- **Call Frequency Patterns**: Detailed call behavior analysis
+- **Customer Service Interactions**: Service call impact on churn rates
+
+### 💰 Revenue Analysis
+- **Revenue Impact Assessment**: Financial implications of customer churn
+- **Revenue Component Breakdown**: Detailed analysis by service type
+- **Customer Segmentation**: Revenue distribution across different customer groups
+
+### 🔗 Correlation Analysis
+- **Interactive Heatmaps**: Feature correlation visualization with Plotly
+- **Churn Predictors**: Features most correlated with customer churn
+- **Statistical Insights**: Positive and negative correlation analysis
+
+### 📊 Advanced Insights
+- **Customer Segmentation**: Usage vs Revenue scatter plots
+- **High-Risk Customer Identification**: Patterns in churned customer behavior
+- **Service Plan Analysis**: Churn rates by plan combinations
+- **Automated Key Insights**: AI-generated business insights and recommendations
+
+## 🛠️ Technology Stack
+
+- **Frontend**: Streamlit for interactive web interface
+- **Visualization**: Plotly for interactive charts and maps
+- **Data Processing**: Pandas and NumPy for data manipulation
+- **Statistics**: Comprehensive statistical analysis and correlation studies
+
+## 📊 Dataset Information
+
+### About the Dataset
+This telecommunications customer churn dataset contains **3,333 customer records** with **21 features** covering:
+
+- **Demographics**: State, account length, area code
+- **Service Plans**: International calling and voice mail plans
+- **Usage Patterns**: Detailed minute usage and call frequency
+- **Billing Information**: Charges breakdown by time period
+- **Customer Service**: Interaction frequency and service quality
+- **Target Variable**: Customer churn status (14.5% churn rate)
+
+### Data Quality
+- ✅ **No Missing Values**: Complete dataset with 100% data coverage
+- ✅ **Geographic Coverage**: All 51 US states represented
+- ✅ **Balanced Features**: Mix of numerical, categorical, and boolean variables
+
+## 📱 Usage Guide
+
+### Navigation
+1. **Sidebar Selection**: Choose between Combined Dataset, Training Set (80%), or Test Set (20%)
+2. **Analysis Types**: Select from 7 comprehensive analysis categories
+3. **Interactive Exploration**: All charts are interactive with hover details and zoom capabilities
+
+### Key Interactions
+- **Hover Information**: Detailed tooltips on all visualizations
+- **Dynamic Filtering**: Real-time updates based on selections
+- **Export Options**: Download charts as images
+- **Responsive Design**: Works seamlessly on desktop and mobile
+
+## 🔍 Key Insights Discovered
+
+### Churn Risk Factors
+- **High Service Calls**: Customers with 4+ service calls have significantly higher churn rates
+- **International Plans**: International plan subscribers show elevated churn risk
+- **Usage Patterns**: Specific usage behaviors correlate with churn likelihood
+- **Geographic Patterns**: Certain states show consistently higher churn rates
+
+### Business Applications
+- **Customer Retention**: Identify at-risk customers for proactive outreach
+- **Service Improvement**: Understand pain points leading to customer churn
+- **Geographic Strategy**: Target retention efforts in high-churn regions
+- **Revenue Protection**: Quantify financial impact and prioritize retention efforts
+
+## 🤝 Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
+
+
+## 📄 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## 🙏 Acknowledgments
+
+- **Dataset**: Telecommunications customer churn data for educational and research purposes
+- **Streamlit**: For providing an excellent framework for data applications
+- **Plotly**: For powerful interactive visualization capabilities
+- **Hugging Face**: For hosting and sharing data science applications
+
+## 📞 Contact & Support
+
+- **Discussions**: [Hugging Face Discussions](https://huggingface.co/spaces/disham993/telecom-churn-dashboard/discussions)
+- **Documentation**: Comprehensive in-app help and tooltips
+
+---
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+**⭐ If you find this project useful, please give it a star!**
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+Built with ❤️ for the data science community