🍳 NLP Preprocessing Cookbook

Welcome to the NLP Preprocessing Cookbook! This repository is a collection of essential techniques for cleaning and preparing text data for Machine Learning models. Think of it as a practical guide with code examples for the most common preprocessing steps in any NLP project.

This entire project is demonstrated within a Jupyter Notebook, making it easy to follow along and experiment with each technique.

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✨ Concepts Covered

This notebook is a step-by-step guide through the foundational techniques of NLP data preparation:

1. Tokenization (Sentence and Word):

   • The first and most crucial step. I've shown how to break down a large block of text into its basic units—sentences and individual words—using the NLTK library.

2. Stemming and Lemmatization:

   • To reduce words to their root forms, I've explored two popular techniques:
     • Stemming: A faster, rule-based approach to chop off word endings.
     • Lemmatization: A more advanced, dictionary-based approach that considers the context to find the true root word (lemma).

3. Stopwords Removal:

   • I've demonstrated how to remove common words (like "is", "the", "a") that add little semantic value, which helps the model focus on the more important keywords.

4. Text Cleaning with RegEx:

   • Real-world text is messy! I've used Regular Expressions (RegEx) to clean the text by removing punctuation, numbers, and other unwanted characters.

5. Text Vectorization (Bag-of-Words):

   • Finally, to make the text understandable for a machine learning model, I've converted the cleaned words into numerical vectors using Scikit-learn's CountVectorizer. This technique, also known as Bag-of-Words, is a fundamental method for feature extraction in NLP.

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🛠️ Libraries Used

• NLTK (Natural Language Toolkit): For tokenization, stemming, lemmatization, and stopwords.
• Scikit-learn: For text vectorization with CountVectorizer.
• Jupyter Notebook: For interactive development and demonstration.

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⚙️ Setup and Installation

1. Clone the repository:

   git clone [https://github.com/jsonusuman351/nlp-preprocessing-cookbook.git](https://github.com/jsonusuman351/nlp-preprocessing-cookbook.git)
   cd nlp-preprocessing-cookbook

2. Create and activate a virtual environment:

   # It is recommended to use Python 3.10 or higher
   python -m venv venv
   .\venv\Scripts\activate

3. Install the required packages:

   pip install notebook nltk scikit-learn

4. One-Time NLTK Downloads: The first time you run this, you'll need to download some necessary models from NLTK. Open a Python interpreter from your activated environment and run:

   import nltk
   nltk.download('punkt')
   nltk.download('stopwords')
   nltk.download('wordnet')
   exit()

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🚀 How to Use This Project

The entire workflow is contained within a single Jupyter Notebook.

1. Launch Jupyter: Make sure your virtual environment is active, then run:

   jupyter notebook

2. Open the Notebook: In the Jupyter interface, click on NLTK-CountVectorizer.ipynb.
3. Run the Cells: You can run each cell sequentially to see how each preprocessing technique transforms the text step-by-step.

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🔬 A Tour of the Cookbook

I've organized this entire project into a single notebook to make it easy to follow my experiments with different NLP preprocessing techniques.

Click to view the code layout

nlp-preprocessing-cookbook/
│
└── NLTK-CountVectorizer.ipynb    # The complete end-to-end workflow is in this single notebook

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