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