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williammanning committed Dec 30, 2024
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4 changes: 3 additions & 1 deletion churn_model_new/README.md
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Expand Up @@ -25,7 +25,8 @@ The primary functionality is to predict the probability of customer churn. The c
2. **Install Dependencies**: Make sure you have the required dependencies installed, as specified in the `conda.yml` file.

```bash
conda env create -f main/conda.yaml
conda config --set ssl_verify false
conda env create -f main/conda.yml
conda activate churn_model
```

Expand All @@ -34,6 +35,7 @@ The primary functionality is to predict the probability of customer churn. The c
```bash
pip install qwak-sdk
qwak configure
pip install "qwak-inference[batch,feedback]"
```

5. **Run the Model Locally**: Execute the following command to test the model locally:
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6 changes: 4 additions & 2 deletions churn_model_new/main/conda.yml
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Expand Up @@ -5,7 +5,9 @@ channels:
dependencies:
- python=3.9
- pip
- pandas
- pandas=2.2.2
- numpy=2.0.0
- scikit-learn
- pybind11>=2.12
- xgboost
- scipy=1.10.1
- scipy
3 changes: 3 additions & 0 deletions churn_model_new/test_model_locally.py
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Expand Up @@ -30,6 +30,9 @@
'Agitation_Level' : 70
}]

# Print that it is starting the process
print("\n\nPREDICTION STARTING:\n\n")

# Create the DataFrame and convert it to JSON
df = DataFrame(feature_vector).to_json()

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