  The rapid growth of the stock market has attracted many investors due to its
potential for significant profits. However, predicting stock prices accurately
is difficult because financial markets are complex and constantly changing.
This is especially true for the cryptocurrency market, which is known for its
extreme volatility, making it challenging for traders and investors to make
wise and profitable decisions. This study introduces a machine learning
approach to predict cryptocurrency prices. Specifically, we make use of
important technical indicators such as Exponential Moving Average (EMA) and
Moving Average Convergence Divergence (MACD) to train and feed the XGBoost
regressor model. We demonstrate our approach through an analysis focusing on
the closing prices of Bitcoin cryptocurrency. We evaluate the model's
performance through various simulations, showing promising results that suggest
its usefulness in aiding/guiding cryptocurrency traders and investors in
dynamic market conditions.
