This project implements a sophisticated news recommender system that harnesses the power of NLTK (Natural Language Toolkit) for robust text processing and utilizes cosine similarity for accurate content-based recommendations. The primary objective of this system is to analyze the textual content of news articles and recommend similar articles based on their semantic similarity, thereby enhancing user engagement and discovery of relevant news content.

The goal of this project is to provide users with personalized news recommendations by identifying articles with similar content. This is achieved through the following steps:

Text Preprocessing: Clean and tokenize news articles, remove stopwords, and apply stemming or lemmatization to normalize text data.

Feature Extraction: Utilize TF-IDF (Term Frequency-Inverse Document Frequency) to convert articles into numerical vectors, capturing the importance of words within each article.

Cosine Similarity Calculation: Compute the cosine similarity between article vectors to quantify their similarity in content.

Recommendation Generation: For a given input article, identify and recommend articles with the highest cosine similarity scores.