machine-failure-prediction-model-rf

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.