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Data Labeling in Machine Learning with Python

You're reading from   Data Labeling in Machine Learning with Python Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781804610541
Length 398 pages
Edition 1st Edition
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Author (1):
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Vijaya Kumar Suda Vijaya Kumar Suda
Author Profile Icon Vijaya Kumar Suda
Vijaya Kumar Suda
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Table of Contents (18) Chapters Close

Preface 1. Part 1: Labeling Tabular Data
2. Chapter 1: Exploring Data for Machine Learning FREE CHAPTER 3. Chapter 2: Labeling Data for Classification 4. Chapter 3: Labeling Data for Regression 5. Part 2: Labeling Image Data
6. Chapter 4: Exploring Image Data 7. Chapter 5: Labeling Image Data Using Rules 8. Chapter 6: Labeling Image Data Using Data Augmentation 9. Part 3: Labeling Text, Audio, and Video Data
10. Chapter 7: Labeling Text Data 11. Chapter 8: Exploring Video Data 12. Chapter 9: Labeling Video Data 13. Chapter 10: Exploring Audio Data 14. Chapter 11: Labeling Audio Data 15. Chapter 12: Hands-On Exploring Data Labeling Tools 16. Index 17. Other Books You May Enjoy

Hands-on text labeling using Logistic Regression

Text labeling is a crucial task in NLP, enabling the categorization of textual data into predefined classes or sentiments. Logistic Regression, a popular machine learning algorithm, proves effective in text classification scenarios. In the following code, we walk through the process of using Logistic Regression to classify movie reviews into positive or negative sentiments. Here’s a breakdown of the code.

Step 1. Import necessary libraries and modules.

The code begins by importing the necessary libraries and modules. These include NLTK for NLP, scikit-learn for machine learning, and specific modules for sentiment analysis, text preprocessing, and classification:

    from nltk.corpus import stopwords
    from nltk.stem import WordNetLemmatizer
    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.model_selection import...
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