| | ---
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| | title: Telco Customer Churn Prediction
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| | emoji: 📞
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| | colorFrom: blue
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| | colorTo: red
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| | sdk: gradio
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| | sdk_version: 4.0.0
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| | app_file: app.py
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| | pinned: false
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| | license: mit
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| | tags:
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| | - machine-learning
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| | - customer-churn
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| | - classification
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| | - business-analytics
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| | - scikit-learn
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| | - telecommunications
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| | datasets:
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| | - telco-customer-churn
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| | metrics:
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| | - accuracy
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| | - precision
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| | - recall
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| | - f1
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| | - roc_auc
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| | pipeline_tag: tabular-classification
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| | ---
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| |
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| | # 📞 Telco Customer Churn Prediction Model
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| |
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| | A machine learning model that predicts customer churn probability for telecommunications companies with **84.2% ROC-AUC** accuracy.
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| |
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| | ## 🚀 Quick Start
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| |
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| | ```python
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| | import joblib
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| | import pandas as pd
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| |
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| | # Load the model
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| | model = joblib.load('model.joblib')
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| |
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| | # Make prediction (requires 45 features)
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| | churn_probability = model.predict_proba(customer_data)[:, 1]
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| | ```
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| |
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| | ## 📊 Model Performance
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| |
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| | | Metric | Score |
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| | |--------|-------|
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| | | **ROC-AUC** | **0.8419** |
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| | | **Accuracy** | 0.8055 |
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| | | **Precision** | 0.6572 |
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| | | **Recall** | 0.5588 |
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| | | **F1-Score** | 0.6040 |
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| |
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| | ## 🎯 Risk Categories
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| |
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| | - 🚨 **High Risk (≥80%)**: 49 customers - *Immediate action required*
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| | - ⚠️ **Medium Risk (50-79%)**: 1,512 customers - *Proactive retention*
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| | - 📊 **Low Risk (30-49%)**: 1,110 customers - *Loyalty programs*
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| | - ✅ **Safe (<30%)**: 4,372 customers - *Upselling opportunities*
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| |
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| | ## 🔧 Features (45 total)
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| |
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| | The model uses 45 features including:
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| | - Demographics (Age, Gender, Partner, Dependents)
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| | - Services (Phone, Internet, Security, Streaming)
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| | - Account (Contract, Payment method, Billing)
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| | - Financial (Tenure, Monthly charges, Total charges)
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| |
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| | ## 💼 Business Impact
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| |
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| | - **Customer Retention**: Identify at-risk customers early
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| | - **Revenue Protection**: Prevent churn-related revenue loss
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| | - **Campaign Optimization**: Target retention efforts effectively
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| | - **ROI**: Expected 300-400% return with targeted campaigns
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| |
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| | ## 🏗️ Model Details
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| |
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| | - **Algorithm**: Logistic Regression
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| | - **Training Data**: 5,634 samples
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| | - **Test Data**: 1,409 samples
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| | - **Training Date**: 2025-07-10
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| |
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| | ## 📋 Usage
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| |
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| | See the demo above for interactive predictions!
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| |
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| | For batch processing:
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| | ```python
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| | # Load model
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| | model = joblib.load('model.joblib')
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| |
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| | # Prepare data with all 45 features
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| | results = model.predict_proba(customer_features)[:, 1]
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| |
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| | # Categorize risk
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| | def categorize_risk(prob):
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| | if prob >= 0.8: return "High Risk"
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| | elif prob >= 0.5: return "Medium Risk"
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| | elif prob >= 0.3: return "Low Risk"
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| | else: return "Safe"
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| | ```
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| |
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| | ## 📜 License
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| |
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| | MIT License - Free for commercial and academic use.
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| |
|
