# kavgan/nlp-in-practice

Starter code to solve real world text data problems. Includes: Gensim Word2Vec, phrase embeddings, Text Classification with Logistic Regression, word count with pyspark, simple text preprocessing, pre-trained embeddings and more.

Repository: https://github.com/kavgan/nlp-in-practice
Canonical: https://ross.abutalabs.com/products/nlp-in-practice
Homepage: http://kavita-ganesan.com/kavitas-tutorials/#.WvIizNMvyog
Language: Jupyter Notebook
License Family: other
Topics: nlp, natural-language-processing, word2vec, text-classification, gensim, tf-idf, machine-learning, text-mining
Last push: 2020-12-02T18:46:45+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3139, "days_push": 2100, "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 1185, forks 785 (observed 2026-08-28T04:03:54.897159+00:00)

## What it is
A collection of Jupyter notebooks and Python scripts demonstrating practical NLP and text mining techniques, including Word2Vec, TF-IDF keyword extraction, text classification, and text preprocessing. It serves as starter code with accompanying tutorials for solving real-world text data problems.

## Use cases
- learn how to train and use gensim word2vec embeddings
- extract keywords from text with tf-idf
- build a text classifier with logistic regression
- preprocess text with stemming, lemmatization, and stop word removal
- do word count on large files with pyspark
- load pre-trained GloVe and word2vec embeddings
- understand tfidftransformer vs tfidfvectorizer

## When to choose
- you want hands-on starter code for common NLP tasks in Python
- you are learning text mining concepts through runnable notebooks
- you need simple examples of word embeddings, TF-IDF, or text classification

## When to avoid
- you need a production-ready NLP library or maintained package
- you require a licensed dependency for commercial use (no license is provided)
- you need up-to-date code, as the repository has not been updated since 2020

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, data-science, data-visualization
- domain: machine-learning, data-science, tutorials
- platform: python
- tags: jupyter-notebooks, word2vec, tf-idf, text-classification, gensim, pyspark, text-preprocessing, word-embeddings, starter-code, natural-language-processing

## Member repositories
- kavgan/nlp-in-practice (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:54.897159+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 2026-08-30T06:24:07.505858+00:00, confidence not recorded.
  - readme: https://github.com/kavgan/nlp-in-practice (fetched 2026-08-28T04:03:54.897159+00:00, sha 2ccc1f91a4cf)
  - homepage: http://kavita-ganesan.com/kavitas-tutorials/#.WvIizNMvyog (fetched 2026-08-29T12:31:17.574084+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.