README.md

metadata

title: Telco Churn Predictor - Production Ready
emoji: 📊
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 4.17.0
app_file: app.py
pinned: false
license: mit

📊 Telco Customer Churn Predictor - Full Featured

Production-ready ML model with 93.19% AUC - Enhanced UI/UX with behavioral features

🚀 Features

👤 Single Customer Analysis

• Interactive sliders for all customer features
• Behavioral features (data usage, support tickets, satisfaction)
• Real-time risk assessment with visual gauge
• Feature importance showing key drivers

📊 Batch Analysis

• CSV upload support for bulk predictions
• Risk distribution visualization
• Downloadable results with probabilities
• Summary statistics for business insights

📈 Business Intelligence

• Risk categorization (High/Medium/Low)
• Actionable insights for retention teams
• Model performance metrics and validation
• Comprehensive documentation and use cases

🎯 How to Use

Single Customer

1. Use sliders to input customer details
2. Include behavioral features for better accuracy
3. Click "Analyze Churn Risk"
4. Review risk level and key factors

Batch Processing

1. Upload CSV with customer data
2. Download results with risk scores
3. Use insights for retention campaigns

📊 CSV Format

Required columns:

• AccountLength, CustServCalls, TotalDayMinutes, TotalDayCalls
• TotalEveMinutes, TotalEveCalls, TotalNightMinutes, TotalNightCalls
• TotalIntlMinutes, TotalIntlCalls, NumberVmailMessages
• InternationalPlan (Yes/No), VoiceMailPlan (Yes/No)
• avg_daily_gb, support_tickets_last_90d, billing_issues_12m, satisfaction_score

🏆 Model Performance

• AUC: 93.19% (validated on Orange Telecom dataset)
• Algorithm: LightGBM with behavioral features
• Validation: Customer-level GroupKFold cross-validation
• Calibration: Brier Score 0.0087 (well-calibrated)

🔧 Technical Details

• Framework: Gradio 4.17.0 (stable version)
• ML Pipeline: Scikit-learn + LightGBM
• Features: Traditional + behavioral patterns
• Deployment: Hugging Face Spaces

💼 Business Value

• Reduce churn by targeting at-risk customers
• Increase revenue through retention campaigns
• Optimize costs with data-driven decisions
• Improve service by understanding pain points

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Built with production-grade ML pipeline and validated on real-world data.