Abstract:

This project presents the development of an intelligent search engine powered by machine learning techniques to enhance the relevance and accuracy of search results. Traditional keyword-based search engines often fall short in understanding the intent and context behind user queries, leading to less effective results. Our system addresses this limitation by incorporating Natural Language Processing (NLP) and machine learning algorithms to analyze user queries semantically, rank documents based on relevance, and continuously improve performance through feedback loops. The search engine architecture integrates key components such as web scraping, text preprocessing, indexing, and a ranking model trained using supervised learning techniques. We employ algorithms such as TF-IDF, Word2Vec, and BERT for document representation and use classification and regression models to improve ranking effectiveness. Additionally, the model adapts to user behavior and preferences over time, ensuring a personalized and efficient search experience. This intelligent search engine not only improves the precision and recall of retrieved documents but also demonstrates scalability and adaptability across multiple domains such as academic research, e-commerce, and legal search systems. The project ultimately showcases how machine learning can significantly enhance the way users retrieve and interact with digital information.

Description:

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