<![CDATA[Cryptocurrency has become an increasingly popular digital asset in recent years. However, cryptocurrency prices are highly volatile and difficult to predict due to being influenced by many factors such as market sentiment, regulations, and technological adoption. This study aims to analyze the performance of several popular machine learning algorithms in accurately predicting cryptocurrency prices. We evaluated four algorithms: Linear Regression, Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) using historical price datasets of Bitcoin, Ethereum, and Litecoin. The data were analyzed by preprocessing steps such as normalization and splitting into training and testing sets. Evaluation metrics used were Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and prediction accuracy. The experimental results showed that the LSTM algorithm had the best performance in predicting cryptocurrency prices with the highest accuracy and lowest error, followed by SVM, Random Forest, and Linear Regression. Further analysis revealed that LSTM was able to capture patterns and trends in complex time series data.]]>
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