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@@ -48,6 +48,7 @@ ham10000_project/
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  ```
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  ### 🔄 Sequence of Execution
 
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  When you run python train_main.py, the system follows this strict sequence to ensure data integrity (preventing leakage) and robust training.
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  Phase 1: Data Preparation
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  Load Metadata (src.data):
@@ -85,6 +86,7 @@ Explainable AI (XAI):
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  Global: Feature Importance plot is generated for Random Forest.
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  Local (LIME): Individual explanations are generated for sample test instances to show why specific decisions were made.
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  Artifact Saving: The best model, scaler, class names, and a training sample (for the App) are saved to models/.
 
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  ### 🚀 How to Run
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  Install Requirements:
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  pip install -r requirements.txt
 
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  ```
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  ### 🔄 Sequence of Execution
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+ ```
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  When you run python train_main.py, the system follows this strict sequence to ensure data integrity (preventing leakage) and robust training.
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  Phase 1: Data Preparation
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  Load Metadata (src.data):
 
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  Global: Feature Importance plot is generated for Random Forest.
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  Local (LIME): Individual explanations are generated for sample test instances to show why specific decisions were made.
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  Artifact Saving: The best model, scaler, class names, and a training sample (for the App) are saved to models/.
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+ ```
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  ### 🚀 How to Run
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  Install Requirements:
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  pip install -r requirements.txt