The project aims to analyze document similarity using natural language processing (NLP) techniques. Document similarity is a crucial task in various domains, including information retrieval, plagiarism detection, and recommendation systems. By quantifying the similarity between documents, can identify related documents, cluster similar documents together, and extract meaningful insights from large text corpora.

Preprocessing : Before analyzing document similarity, the text data undergoes preprocessing steps, including:

• Tokenization: Breaking down the text into individual words or tokens.
• Lowercasing: Converting all text to lowercase to ensure consistency.
• Stopword Removal: Removing common words (e.g., "the", "is", "and") that do not carry significant meaning.
• Lemmatization or Stemming: Normalizing words to their base or root forms to reduce inflectional variants.

Vectorization: Vectorization transforms text data into numerical vectors that machine learning algorithms can process. Common techniques include:

• Bag-of-Words (BoW): Representing each document as a vector of word counts.
• TF-IDF (Term Frequency-Inverse Document Frequency): Weighing terms based on their frequency in the document and their rarity across the corpus.
• Word Embeddings: Representing words as dense vectors in a continuous vector space, capturing semantic relationships between words.

Once the documents are vectorized, cosine similarity is used. Cosine Similarity: Measures the cosine of the angle between two vectors, indicating the similarity in direction.