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Stock Market Analysis Using Linear Regression and Decision Tree Regression

机译:使用线性回归和决策树回归的股票市场分析

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In business, the Stock market or Share market is a more perplexing and sophisticated way to do business. Every business owner wants to reduce the risk and make an immense profit using an effective way. The bank sector, brokerage corporations, small ownerships, all depends on this very body to earn profit and reduce risks. However, using the machine learning algorithm of this paper to predict the future stock price and shuffle by using subsist algorithms and open source libraries to assist in inventing this unsure format of business to a bit more predictable. The proposed system of this paper works in two methods - Linear Regression and Decision Tree Regression. Two models like Linear Regression and Decision Tree Regression are applied for different sizes of a dataset for revealing the stock price forecast prediction accuracy. Moreover, the authors of this paper have revealed some development that could be the club to acquire better validity in these approaches.
机译:在商业中,股票市场或股市是一种更令人困惑和复杂的商业方式。 每个企业所有者都希望利用有效的方式降低风险并使巨大的利润。 银行部门,经纪公司,小型所有权,一切都取决于这个非常赚取的盈利和减少风险。 但是,使用本文的机器学习算法通过使用本地算法和开源库来预测未来的股票价格和随机播放,以帮助发明这一不确定的业务格式,以更具可预测的方式。 本文所提出的系统有两种方法 - 线性回归和决策树回归。 两个模型,如线性回归和决策树回归被应用于不同大小的数据集,用于揭示股票价格预测预测准确性。 此外,本文的作者揭示了一些可能成为俱乐部在这些方法中获得更好的有效性。

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