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@article{165310,
        author = {ARUN KUMAR SHARMA and PARTH SINGH and Dushyant Sharma and Mohit Yadav},
        title = {Shop Price Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {11},
        number = {1},
        pages = {1068-1073},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165310},
        abstract = {In the dynamic landscape of retail, the accurate prediction of shop prices has emerged as a critical factor influencing both retailers and consumers. This abstract presents a succinct overview of a project that employs machine learning techniques to revolutionize shop price prediction, resulting in optimized pricing strategies and improved consumer experiences. The project explores the application of machine learning algorithms to harness extensive and diverse datasets, including historical pricing data, product attributes, and external market variables. These algorithms, ranging from regression models to neural networks, enable retailers to adapt to ever-changing market conditions, capture intricate pricing patterns, and make real-time adjustments. Additionally, they empower retailers to offer personalized pricing strategies, tailoring prices to individual customer preferences. Ethical considerations are woven into the project's fabric to ensure that pricing practices are fair, transparent, and in compliance with legal and ethical standards. This undertaking has far-reaching significance, promising increased profitability for retailers, competitive advantages, and an enhanced shopping experience for consumers. In a time when transparency, fairness, and adaptability in pricing strategies are paramount, the implementation of machine learning in shop price prediction stands as a transformative force. This project serves as a comprehensive guide to understanding the methodologies, challenges, and ethical dimensions that underpin the integration of machine learning, ultimately leading to pricing strategies that are not only data-driven but also customer-centric and ethically sound.},
        keywords = {},
        month = {},
        }