Designed a scalable & efficient search engine using JavaScript, Python, and Docker capable of handling 10 million queries per second. Leveraged advanced algorithms such as TF-IDF and vector space model to increase keyword relevance scoring by 40% and improve query matching accuracy by 25%. Executed an innovative inverse indexing system in conjunction with SQL/NoSQL databases to decrease document retrieval time by 60%, resulting in a more seamless user experience.
This project addresses the growing demand for high-speed, large-scale search capabilities in modern applications. The system architecture allows for horizontal scaling, ensuring that performance remains consistent under heavy loads. With a focus on delivering accurate and relevant results, the search engine optimizes both data retrieval time and user satisfaction.