Utilizing Deep Learning and Machine Learning Methods to Forecast Market Performance

Forecasting the stock market has become one of the most challenging problems for the AI research community. Stock market investment strategies are complex and depend on analyzing amounts of data. Recently, machine learning techniques have increasingly evaluated and enhanced market predictions compared to traditional methods. This interest is driven by the time-dependent nature of stock prices, making their predictions essential in data mining and a subject of significant interest and effort over the past decades. This challenge remains difficult due to the intrinsic characteristics of time series data, such as its high dimensionality, large volume, and constant updates. Researchers have explored machine learning and deep learning methods to improve the effectiveness of traditional approaches. In this document, we aim to forecast stock market performance at the end of the trading day by applying various machine learning algorithms to two datasets, “CoinMarketCap” and “CryptoCurrency,” and analyzing the prediction outcomes of these models.