The auto sector stock price trend is influenced by a number of national and worldwide unknown elements. It takes into account both local and global influences. The influence of such a factor on the stock price trend is difficult to forecast because the impact of the same factor fluctuates over time and due to the nonlinear character of the financial stock market. One of the strategies for capturing trends in historical data is machine learning. This feature is what inspired us to apply it in this study to forecast the auto sector stock price trend. It suggests a thorough investigation into the auto sector’s stock price trend forecasting. This study focuses on decision tree classifier as a method for forecasting trend in the auto industry price prediction since it can handle multidimensional data and does not require domain knowledge.
Author(s) Details
Manish M. Goswami
Department of Information Technology, Rajiv Gandhi College of Engineering & Research, Nagpur, India.
Sachin Jambhulkar
Department of Electronics & Communication, Vidarbha Institute of Technology, Nagpur, India.
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