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README.md
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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):
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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
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