Financial Risk Management in Cryptocurrency Markets Using Machine Learning

The present study employs machine learning methodologies to assess and mitigate financial risks inside the bitcoin industry. For traders and investors, the erratic nature of cryptocurrencies presents formidable obstacles. The study models and forecasts market trends and dangers using algorithms like Random Forest, Linear Regression, and Neural Networks. Recursive Feature Elimination (RFE) is one feature selection technique that is used to find important indications that impact market behavior. A Raspberry Pi can be used to implement the suggested model for decision assistance and risk analysis in real time. The findings show that machine learning can offer insightful information about market dynamics, which can help with risk management and investment strategies.