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--- |
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title: Machine Failure Prediction with SHAP |
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emoji: 🤖🔧 |
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colorFrom: blue |
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colorTo: green |
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sdk: gradio |
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sdk_version: 4.12.0 |
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python_version: 3.1 |
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app_file: app.py |
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pinned: false |
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license: mit |
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language: |
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- en |
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--- |
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# Machine Failure Prediction with Live SHAP Analysis |
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This application demonstrates a real-time machine failure prediction model. It allows users to interactively adjust machine parameters and see the immediate impact on the predicted failure probability. |
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## How it Works |
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The app uses a `RandomForestClassifier` model trained on a predictive maintenance dataset. For each prediction, it uses the **SHAP (SHapley Additive exPlanations)** library to explain the output. |
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- **Probability of Machine Failure**: The model's prediction of the likelihood that the machine will fail given the current input parameters. |
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- **SHAP Waterfall Plot**: This plot visualizes how each feature contributes to pushing the prediction away from the baseline (the average prediction) towards the final probability. |
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- <span style="color:red;">**Red bars**</span> show features that are increasing the probability of failure. |
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- <span style="color:blue;">**Blue bars**</span> show features that are decreasing the probability of failure. |
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## How to Use the Demo |
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1. **Adjust the Sliders**: Use the sliders and the dropdown on the left to change the input values for the machine's operational parameters. |
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2. **View Real-Time Results**: The "Probability of Machine Failure" and the SHAP plot on the right will update automatically. |
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3. **Interpret the Plot**: Observe how changing a feature (e.g., increasing 'Tool wear') changes its contribution (the size and color of its bar) in the SHAP plot. |
