Create README.md

README.md

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+---
+title: Machine Failure Prediction with SHAP
+emoji: 🤖🔧
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 4.12.0
+python_version: 3.1
+app_file: app.py
+pinned: false
+license: mit
+language:
+- en
+---
+
+# Machine Failure Prediction with Live SHAP Analysis
+
+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.
+
+## How it Works
+
+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.
+
+-   **Probability of Machine Failure**: The model's prediction of the likelihood that the machine will fail given the current input parameters.
+-   **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.
+    -   <span style="color:red;">**Red bars**</span> show features that are increasing the probability of failure.
+    -   <span style="color:blue;">**Blue bars**</span> show features that are decreasing the probability of failure.
+
+## How to Use the Demo
+
+1.  **Adjust the Sliders**: Use the sliders and the dropdown on the left to change the input values for the machine's operational parameters.
+2.  **View Real-Time Results**: The "Probability of Machine Failure" and the SHAP plot on the right will update automatically.
+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.