Please add categories to keep this neat
- EML + eml-parser
- mailparser.io
- Python Libs
- Line by line example of reading in emails (https://www.thepythoncode.com/article/reading-emails-in-python)
- Extra Python Lib (https://pypi.org/project/mail-parser/)
- Frequency analysis: phrases or words
- URL Links
- Recipient list: similarities between recipient names/emails
- Sentiment analysis with TextBlob
- Readability metrics with textstat
- Grammar metrics with grammar-check
- Matplotlib documentation
- Matplotlib is a well-documented library that can visualize data well as well as animate it.
- Plotly documentation
- Many different ways to visualize data through easy-to-understand charts/figures.
- Pygal documentation
- Simple, but easy to understand visual representations of data with easy-on-the-eyes color settings.
- Gleam documentation
- Allows for viewer to edit the way they view the data, can be useful for determining how we want to visualize our data, not really for presentation.
- Leather documentation
- Simple and easy to understand data visualization. Good for presentation or representing basic data.
- Pandas documentation
- Aids in reading data into data structures you can add to or manipulate. You can also calculate certain values depending on how much or what type of data you have in the dataset.
- A full Email Header Analysis doc(https://mlhale.github.io/nebraska-gencyber-modules/phishing/email-headeranalysis/#email-headers)