---
title: Customer Churn Survival Analysis
emoji: 🔍
colorFrom: yellow
colorTo: indigo
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
short_description: Customer Churn Prediction and Explainability
python_version: "3.12"
---

# 🔍 Customer Churn Survival Analysis

Complete analysis of churn risk using **XAI (SHAP)** and **Survival Analysis**.

## 🎯 Features

- **Explainable AI**: It explains *why* a customer has been classified as churner or non-churner, given the information present in the dataset
- **Survival Analysis**: It predicts *when* a customer is likely to churn and *how much* risk there is
- **Interactive Visualizations**: Plots shown with Gradio

## 🚀 How to Use It

1. Choose between "Random Customer" or "Specific Customer"
2. Click on "Analyze Customer" 
3. Explore the results in 3 tabs: 
   - **XAI**: SHAP values
   - **Survival Analysis**: Risk timeline and Survival Probability Distribution through time
   - **Details**: Customer Feature Values

## 🛠️ Models

- **XGBoost Classifier**: Churn Prediction 
- **Cox Proportional Hazards**: Survival analysis
- **SHAP**: Explainability

## 📊 Dataset

Bank Customer Churn dataset with 200 test customers.