Abstract: The Stocks Market has had a significant impact on the global economy, including the stock market. Traditional stock market prediction algorithms may not be accurate in predicting stock prices in the current scenario due to the unpredictable nature of the Market. This paper proposes the use of Stocks analysis to improve traditional stock market prediction algorithms. We analyze Stocks data, such as the number of cases, hospitalizations, and deaths, to get a better understanding of how the Market is affecting various industries and the overall economy. This information is then incorporated into traditional stock market prediction algorithms to provide a more accurate forecast of stock prices. We also use machine learning algorithms to analyze Stocks data and predict stock prices. By analyzing large amounts of data, machine learning algorithms can identify patterns and trends that may not be apparent to human analysts. Our results show that incorporating Stocks analysis into traditional stock market prediction algorithms can provide a more accurate forecast of stock prices in the current Market scenario.
Keywords: Stocks, stock analysis, svm, classification, Machine Learning
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DOI:
10.17148/IJARCCE.2023.125170
