<![CDATA[Cryptocurrency price volatility requires predictive models capable of accurately capturing non-linear patterns. This study predicts the price of Bitcoin (BTCUSDT) as the main asset, as well as Ethereum (ETHUSDT) and Ripple (XRPUSDT) as comparison assets, using Decision Tree, Random Forest, XGBoost, and LSTM models. The novelty of this study lies in the analysis of temporal data leakage and the evaluation of model extrapolation capability within a uniform experimental framework. Daily historical data were processed through cleaning, correlation analysis, variable selection, and sequential 70:30 data splitting. The prediction target was defined as the next-day closing price to avoid data leakage, and the models were evaluated using time-series cross-validation with RMSE, MAPE, and R² metrics. The results show that the best-performing model differs for each asset: LSTM outperformed other models for BTC and XRP, while Random Forest performed best for ETH, with R² values ranging from 0.60 to 0.98. Tree-based models tended to produce flat predictions when test prices exceeded the training data range. These findings emphasize the importance of defining prediction targets, applying temporal validation, and conducting cross-asset evaluation in selecting appropriate models for cryptocurrency price prediction.]]>
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