Abstract: The project implements a local machine learning system that uses Python to predict cryptocurrency prices through lightweight operations. The system unites data science flexibility with regression-based learning models to enable users to execute complete offline predictive model training and evaluation and deployment without server-based or cloud-based computing needs. Through its CSV data-input capability the application provides strong data preprocessing and analysis as well as an interface to try several regression models including Linear Regression Decision Trees and Random Forests. The system incorporates visualization components together with evaluation metrics to boost interpretability and usability features. Users can access the system through a basic interface which provides easy avenues for adding real-time feeds and deep learning models in addition to current capabilities. The paper examines the main framework design and data processing systems while outlining potential upgrades to validate easy-to-implement offline prediction technologies for business forecasting.
Keywords: Cryptocurrency, Machine Learning, Regression Models, Offline Prediction, Financial Forecasting
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DOI:
10.17148/IJARCCE.2025.14475
