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c94dda2 b07b05a | 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 56 57 58 59 | # 🧠 TabTransformer Multitask Model for Churn, Tenure, and LTV Prediction
This model is a multitask `TabTransformer` implemented in PyTorch, designed to perform:
- **Binary classification** for customer **churn**
- **Regression** for customer **tenure**
- **Regression** for customer **LTV (Lifetime Value)**
It is saved as a pickle file: `model.pkl` and includes all custom layers (e.g., positional encoding).
---
## 🧩 Model Architecture
- Tabular input with:
- `x_num`: Numerical features (projected into latent space)
- `x_cat`: Categorical features (embedded + transformer)
- Transformer-based attention over categorical embeddings
- Multi-head output for multitask predictions:
- `Churn`: Sigmoid activation for binary classification
- `Tenure` and `LTV`: Linear regression heads
---
## 🧪 How to Use
### 1. Install Dependencies
```bash
pip install torch pandas
import torch
# Load the model from pickle
with open("model.pkl", "rb") as f:
model = torch.load(f)
model.eval()
# Example dummy input
x_num = torch.rand((1, 10)) # Replace 10 with your actual num_features
x_cat = torch.randint(0, 5, (1, 3)) # Replace with your actual number of categories
# Predict
churn_prob, predicted_tenure, predicted_ltv = model(x_num, x_cat)
print("Churn probability:", churn_prob.item())
print("Predicted tenure:", predicted_tenure.item())
print("Predicted LTV:", predicted_ltv.item())
(
churn_prob: FloatTensor of shape (B, 1),
predicted_tenure: FloatTensor of shape (B, 1),
predicted_ltv: FloatTensor of shape (B, 1)
)
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