NLTK vs. OpenAI vs. Upgini: A Comparative Study of Machine Learning Prediction Accuracy Based on Python Library-Generated Features

There are several Python libraries that can be used to generate features from text data for ML modeling. Each library has its own unique features, functionality, and learning curve. It does take some time and effort to study and understand the specifics of each library and how to use them for text feature extraction effectively. I want to share with you the results of my comparison of the three libraries and the accuracy of the ML model using the extracted features.

Photo by Kevin Canlas on Unsplash

Benchmark objective

The subject of this benchmark is to determine a Python library that generates the most powerful features from the text for ML models.

Benchmark participants

The three approaches to feature generation from texts that we will compare:

• NLTK (Natural Language Toolkit) is a popular Python library that provides tools…