DUOINSPECT- Uncovering Duplicates through Powerful Model of Random Forest and XGBoost Classifier

Duplicate question pair detection plays a vital role in improving information retrieval systems and enhancing user experience. In this paper, we present comprehensive study on duplicate utilizing the Quora dataset. We employed machine learning techniques, specifically Random Forest XGBoost classifiers, to develop accurate models for identifying pairs. To improve performance of models, introduced additional features dataset, augmenting original data. By incorporating 22 extra derived from raw data, aimed capture more nuanced patterns increase models' discriminatory power. The model achieved significant improvement, with boost 89.4% accuracy compared initial 73% accuracy. classifier also showed promising results, achieving an 73.4% initially 79.2% after features. This paper serves as valuable reference researchers practitioners interested field detection. findings highlight effectiveness classifiers combination task. web application provides practical user-friendly tool real- time detection, offering potential applications systems, chatbots, question-answering platforms. Key Words: Question Pair Similarity; XGBoost, Forest, Machine Learning.