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487ec3c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | # 🐱 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 |
---
## 💾 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])
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