Information Retrieval System

This project is an Information Retrieval System designed to efficiently search and retrieve relevant information from a large collection of documents.

The system allows users to enter a search query, then processes the text data and returns the most relevant documents based on similarity and keyword matching. The goal of the system is to help users quickly find useful information from large datasets.

Key Features:

Text preprocessing (tokenization, stop-word removal, normalization)

Keyword-based search functionality

Document ranking based on relevance

Fast retrieval of relevant documents

User-friendly search interface

Technologies Used:

Python

Natural Language Processing (NLP) techniques

TF-IDF for text representation

Cosine Similarity for ranking results

Libraries such as NumPy and Pandas

Outcome:

The system successfully retrieves and ranks documents according to the user's query, improving the efficiency of searching within large text datasets.