README.md

# 🐱 CatBoost Models for Churn, Tenure, and LTV Prediction

This repository contains three CatBoost models trained to predict:

- **Churn** (`clf_churn.pkl`) – Binary classification (likelihood of customer churn)
- **Tenure** (`RegTenure.pkl`) – Regression (expected number of months a customer stays)
- **Lifetime Value (LTV)** (`reg_ltv.pkl`) – Regression (predicted total value of a customer)

Each model is saved using Python's `pickle` module and can be loaded easily for inference.

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## 🧠 Model Overview

| Model File        | Task               | Type           |
|-------------------|--------------------|----------------|
| `clf_churn.pkl`   | Churn Prediction    | Classification |
| `RegTenure.pkl`   | Tenure Estimation   | Regression     |
| `reg_ltv.pkl`     | LTV Prediction      | Regression     |

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## 💾 How to Use

### 1. Install Requirements

```bash
pip install catboost pandas


import pickle

with open("clf_churn.pkl", "rb") as f:
    clf_cb = pickle.load(f)

with open("RegTenure.pkl", "rb") as f:
    reg_tenure_cb = pickle.load(f)

with open("reg_ltv.pkl", "rb") as f:
    reg_ltv_cb = pickle.load(f)


# Predict churn probability
churn_proba = clf_cb.predict_proba(X_test)[:, 1]

# Predict tenure
tenure_pred = reg_tenure_cb.predict(X_test)

# Predict lifetime value
ltv_pred = reg_ltv_cb.predict(X_test)

print("🔁 Churn:", churn_proba[:5])
print("📅 Tenure:", tenure_pred[:5])
print("💰 LTV:", ltv_pred[:5])