Auto Sector Stock Price Trend Prediction using Decision Tree
Manish M. Goswami
Manish M. Goswami*,Department of Information Technology, Rajiv Gandhi College of Engineering and Research, Nagpur, India.
Manuscript received on April 01, 2020. | Revised Manuscript received on April 07, 2020. | Manuscript published on May 30, 2020. | PP: 1131-1134 | Volume-9 Issue-1, May 2020. | Retrieval Number: F9882038620/2020©BEIESP | DOI: 10.35940/ijrte.F9882.059120
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Decision Tree, Supervised Learning, Unsupervised Learning.
Scope of the Article: Regression and Prediction