break down the analysis and proposal:

Key Assessment Requirements:

• 3000-word report focusing on NLP/feature engineering
• Weekly logged progress
• Clear separation from classification tasks
• Technical implementation must match research questions

Current Structure Issues:

1. Code duplication between notebooks and .py files
2. Unclear separation of concerns
3. Missing standardized logging across components
4. Scattered feature engineering logic

Proposed Reorganization:

1. Core Pipeline Structure:

scripts/
├── pipeline/
│   ├── text_processor.py      # Core NLP processing
│   └── enhanced_preprocessor.py # Advanced feature extraction
├── utils/
│   ├── db_utils.py           # Database operations
│   ├── preprocessing_logger.py # Centralized logging
│   └── viz_utils.py          # Visualization helpers

1. Notebook Refactoring: initial_data_exploration.ipynb should only contain:

import sys
sys.path.append('../scripts/utils')
from preprocessing_logger import PreprocessingLogger
from db_utils import DatabaseUtils
logger = PreprocessingLogger(log_dir='../logs')
db = DatabaseUtils()
# Analysis code (10-20 lines max)results = db.query_database("SELECT...")

Recommendations:

1. Move all processing logic to .py files
2. Notebooks should only contain analysis code
3. Use standardized logging across all components
4. MCP integration for database operations