Create README.md

README.md

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+# 🐱 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.
+
+---
+
+## 🧠 Model Overview
+
+| Model File        | Task               | Type           |
+|-------------------|--------------------|----------------|
+| `clf_churn.pkl`   | Churn Prediction    | Classification |
+| `RegTenure.pkl`   | Tenure Estimation   | Regression     |
+| `reg_ltv.pkl`     | LTV Prediction      | Regression     |
+
+---
+
+## 💾 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])