This repository contains Python code for extracting and transforming text features from raw text data, designed to support natural language processing (NLP) and machine learning workflows.
Text feature engineering involves transforming text into structured numerical formats that models can use effectively. Common techniques include tokenization, vectorization, n-grams, term frequency–inverse document frequency (TF-IDF), and more.
You’ll find code in the src/ directory to preprocess text, generate features, and experiment with feature representations.
The data/ folder contains example data used for demonstrations and testing.
Techniques may include word-level and character-level features, statistical representations, and vectorization pipelines.
Feature engineering turns text into numbers that models understand. Typical steps include:
- Cleaning and normalizing raw text (lowercasing, removing punctuation)
- Converting text to tokens (words or subwords)
- Building numerical representations like bag-of-words or TF-IDF vectors
These engineered features can dramatically improve performance in text classification, clustering, and other NLP tasks.
- Text classification (spam detection, sentiment analysis)
- Document clustering
- Topic modeling
- Downstream machine learning pipelines