Required Libraries

pandas numpy matplotlib seaborn scikit-learn beautifulsoup4 requests ast re

Usage

1. Scraping Data

python scraper.py

python scraper_svg_improved.py

python scraper_tag.py

2. Data Cleaning

Process raw scraped data:

python clean_data.py data_and_output/bracelet_book_improved.csv

3. Main Analysis

Open and run the analysis notebooks:

jupyter notebook analyse_data.ipynb

Key sections:

• Basic data exploration and visualization
• Correlation analysis between complexity and engagement
• PCA and clustering analysis
• Linear regression and Random Forest modeling

4. Machine Learning Experiments

Explore pattern generation approaches:

jupyter notebook analyse_data_ml.ipynb

Features:

• Positional feature extraction from knot data
• Multi-label tag classification
• Pattern structure analysis for generation