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@article{197531,
        author = {Mrs.Dhanshree Shinde and Ms. Pranali Bobaade and Anuja Mokampalle and Bhavesh Mundhe},
        title = {XAUUSD PRICE PREDICTION AND RECOMMENDATION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7196-7199},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197531},
        abstract = {In financial markets, predicting commodity prices such as gold is an important task for traders and investors. Gold is represented as XAUUSD in the forex market, which indicates the price of gold in US dollars. This paper presents a machine learning-based system for predicting XAUUSD prices and providing trading recommendations.
The proposed system analyzes historical gold price data and applies machine learning algorithms such as Linear Regression to forecast future price movements. The system processes features such as open price, close price, high price, low price, and trading volume to identify patterns in the data.
Based on predicted trends, the system provides simple trading recommendations such as buy, sell, or hold to assist traders in making better investment decisions. The system helps improve prediction accuracy and simplifies the process of analyzing gold market trends.},
        keywords = {Gold price prediction, XAUUSD, Machine Learning, Financial forecasting, Recommendation system.},
        month = {April},
        }