Text similarity analysis is a common NLP task that involves quantifying how similar or related two or more pieces of text are. In this project, I use techniques such as Count Vectorization and TF-IDF Vectorization to transform text data into numerical features and calculate cosine similarity to measure the similarity between documents.
This repository contains Python code for performing text similarity analysis using natural language processing (NLP) techniques. The code utilizes libraries like Pandas, Scikit-Learn, NLTK, and Seaborn to analyze the similarity between different documents based on their content.