| # 🧠 TabTransformer Multitask Model for Churn, Tenure, and LTV Prediction |
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| This model is a multitask `TabTransformer` implemented in PyTorch, designed to perform: |
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| - **Binary classification** for customer **churn** |
| - **Regression** for customer **tenure** |
| - **Regression** for customer **LTV (Lifetime Value)** |
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| It is saved as a pickle file: `model.pkl` and includes all custom layers (e.g., positional encoding). |
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| ## 🧩 Model Architecture |
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| - 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 |
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| ## 🧪 How to Use |
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| ### 1. Install Dependencies |
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| ```bash |
| pip install torch pandas |
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| import torch |
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| # Load the model from pickle |
| with open("model.pkl", "rb") as f: |
| model = torch.load(f) |
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| model.eval() |
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| # 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 |
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| # Predict |
| churn_prob, predicted_tenure, predicted_ltv = model(x_num, x_cat) |
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| print("Churn probability:", churn_prob.item()) |
| print("Predicted tenure:", predicted_tenure.item()) |
| print("Predicted LTV:", predicted_ltv.item()) |
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| ( |
| 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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