# susanli2016/NLP-with-Python

Scikit-Learn, NLTK, Spacy, Gensim, Textblob and more

Repository: https://github.com/susanli2016/NLP-with-Python
Canonical: https://ross.abutalabs.com/products/nlp-with-python
Language: Jupyter Notebook
License Family: other
Last push: 2024-03-28T14:37:21+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3317, "days_push": 888, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2791, forks 1979 (observed 2026-08-28T04:07:22.105783+00:00)

## What it is
A collection of Jupyter Notebook tutorials covering natural language processing with Python libraries such as Scikit-Learn, NLTK, SpaCy, Gensim, and TextBlob. It serves as a hands-on learning resource with example notebooks for common NLP tasks.

## Use cases
- learn nlp with python notebooks
- text classification tutorial scikit-learn
- topic modeling with gensim example
- named entity recognition with spacy notebook
- sentiment analysis with textblob example
- nltk tutorial for beginners

## When to choose
- you want runnable notebook examples for common NLP tasks
- you are learning Python NLP libraries like NLTK, SpaCy, or Gensim
- you need reference code for text classification or topic modeling

## When to avoid
- you need a production-ready NLP library or framework
- you require a maintained, licensed package with API guarantees
- you need deep-learning-focused NLP with transformers

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, data-science
- domain: machine-learning, tutorials
- platform: python
- tags: jupyter-notebooks, scikit-learn, nltk, spacy, gensim, textblob, tutorials, natural-language-processing

## Member repositories
- susanli2016/NLP-with-Python (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:22.105783+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:15:16.672170+00:00, confidence not recorded.
  - readme: https://github.com/susanli2016/NLP-with-Python (fetched 2026-08-28T04:07:22.105783+00:00, sha 9149befb5510)
- Data as of 2026-08-30T08:39:29.467469+00:00.