Auto Sector Stock Price Trend Prediction using Decision Tree

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Abstract

The auto sector stock price trend is based on many national and international uncertain factors. It is challenging to predict the impact of such a factor on the stock price trend as the impact of the same factor varies at different points of time. In this research work, we are predicting the auto sector stock price trend using patterns in the historical data using a machine learning method.
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基于决策树的汽车行业股价走势预测
汽车行业的股价走势受到许多国内和国际不确定因素的影响。由于同一因素在不同时间点的影响是不同的,因此预测这一因素对股价走势的影响是具有挑战性的。在这项研究工作中,我们使用机器学习方法使用历史数据中的模式来预测汽车行业的股价趋势。
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