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@article{193148,
        author = {Venkat Badavath and B Akshaya and J Deekshith and C Kavitha},
        title = {Forecasting Crypto Prices using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {3801-3806},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193148},
        abstract = {The price of cryptocurrencies is hard to predict because the market is very unstable and there are many economic, social, and regulatory factors that affect it. This project uses a structured machine learning method to guess the prices of cryptocurrencies using historical data from Investing.com, including open, high, low, and close values spanning multiple years. The data underwent preprocessing steps such as chronological reordering, handling missing or redundant entries, and renaming columns for clarity. Date fields were transformed into features like day, month, and year to capture temporal trends. Development was conducted in Jupyter Notebook using Python, with libraries such as pandas and NumPy for data manipulation and Matplotlib and seaborn for visualization. The predictive model was built using XGBoost, a gradient boosting algorithm known for its performance on tabular data. The model was serialized using Python’s pickle module for easy deployment in future applications. Overall, the project highlights how machine learning, when combined with thoughtful preprocessing and feature engineering, can provide valuable insights and predictions in the unpredictable cryptocurrency market.},
        keywords = {Cryptocurrency, Price Prediction, Machine Learning, XGBoost, Time Series Forecasting, Data Preprocessing, Feature Engineering, Historical Data, Python, Jupyter Notebook, Pandas, NumPy, Matplotlib, Seaborn, Gradient Boosting, Model Serialization, Pickle, Financial Data Analysis, Investing.com Dataset, Predictive Modeling.},
        month = {February},
        }