1. Project Strategy Analysis Our project has two clear focal points:

• Feature Engineering (CLD6000 focus)
• Data Processing Pipeline Development

Key Components:

# Core Processing Chain
Data Import -> Preprocessing -> Feature Engineering -> Analysis

1. Assessment-Driven Architecture The project specifically targets:

• Natural Language Processing features
• Document structure analysis
• Performance monitoring
• Clear separation from classification tasks

1. Current Structure Analysis:

/scripts/
├── pipeline/          # Core processing logic
│   ├── text_processor.py    # Primary text processing
│   └── enhanced_preprocessor.py  # Advanced features
├── utils/
│   ├── preprocessing_logger.py   # Logging infrastructure
│   ├── data_preprocessor.py     # Initial data processing
│   ├── db_utils.py             # Database operations
│   └── viz_utils.py            # Visualization utilities
└── import_data.py     # Data ingestion

1. Integration Strategy: Our notebooks should be thin interfaces that leverage the processing pipeline:

# initial_data_exploration.ipynb
from scripts.pipeline.text_processor import TextProcessor
from scripts.utils.viz_utils import plot_distributions

processor = TextProcessor()
features = processor.extract_features(text_data)
plot_distributions(features)

1. Modular Design Principles: Each component has a single responsibility:

• text_processor.py: Core text analysis
• enhanced_preprocessor.py: Advanced feature extraction
• data_preprocessor.py: Initial data cleaning
• import_data.py: Data ingestion