TF-IDF (Term frequency, Inverse Document Frequency) is an algorithm or way to score the importance of words (or 'terms') based on how frequently they appear

which means

• If a word appears frequently in a document, it's important. Give the word a high score.
• But if a word appears in many documents, it's not a unique identifier. Give the word a low score.

Before using this TF-IDF implementation, ensure you have the following packages installed:

• textblob
• nltk

You can install these packages using pip: 'pip install textblob nltk'

This implementation of TF-IDF features improvements such as:

• Utilizing NLTK to download stopwords and tokenize the text.
• Filtering out stopwords from the document before calculating TF-IDF scores.
• Lowercasing the words to ensure case insensitivity.
• Calculating TF-IDF scores based on the filtered document.

• Ensure you have Python installed on your system.
• Install the required packages using pip as mentioned in the Prerequisites section.
• Clone or download this repository.
• Navigate to the directory containing the TF-IDF script.
• Run the script and follow the prompts to enter the location of the document file.

The script will calculate the TF-IDF scores and display the top words along with their scores.

Two example text files have been provided in the repository for testing the TF-IDF algorithm.

• text.txt
• text2.txt

• For more information on TF-IDF and its applications, visit the following link:

• TF-IDF Explained - Steven Loria

• This project is licensed under the MIT License - see the LICENSE file for details.