| # 🐱 CatBoost Models for Churn, Tenure, and LTV Prediction |
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| This repository contains three CatBoost models trained to predict: |
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| - **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) |
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| Each model is saved using Python's `pickle` module and can be loaded easily for inference. |
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| ## 🧠 Model Overview |
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| | 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 |
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| ### 1. Install Requirements |
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| ```bash |
| pip install catboost pandas |
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| import pickle |
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| with open("clf_churn.pkl", "rb") as f: |
| clf_cb = pickle.load(f) |
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| with open("RegTenure.pkl", "rb") as f: |
| reg_tenure_cb = pickle.load(f) |
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| with open("reg_ltv.pkl", "rb") as f: |
| reg_ltv_cb = pickle.load(f) |
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| # Predict churn probability |
| churn_proba = clf_cb.predict_proba(X_test)[:, 1] |
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| # Predict tenure |
| tenure_pred = reg_tenure_cb.predict(X_test) |
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| # Predict lifetime value |
| ltv_pred = reg_ltv_cb.predict(X_test) |
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| print("🔁 Churn:", churn_proba[:5]) |
| print("📅 Tenure:", tenure_pred[:5]) |
| print("💰 LTV:", ltv_pred[:5]) |
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