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Stock Price Prediction Using Linear Regression, LSTM and Decision Tree

EasyChair Preprint no. 7805

4 pagesDate: April 18, 2022


In the cash world stock trading is maybe the principle development. Protections trade assumption is a showing of endeavoring to conclude the future worth of a stock other financial instrument traded on a money related exchange. This paper explains the gauge of a stock using Machine Learning. The particular and head or the time series assessment is used by a huge piece of the stockbrokers while making the stock assumptions. The programming language is used to anticipate the protections trade using AI is Python. In this paper we propose a Machine Learning (ML) approach that will be ready from the available stocks data and gain knowledge and a short time later includes the acquired data for an exact figure. In this setting this study uses an AI strategy called Linear Regression, LSTM and Decision Tree to anticipate stock expenses for the immense and little capitalizations and in the three special business areas, using costs with both step by step and approved frequencies.

Keyphrases: Data Analytics, Decision Tree, linear regression, LSTM, Machine Learning Algorithms, stock market

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Sarthak Singh and Shaurya Rehan and Vimal Kumar},
  title = {Stock Price Prediction Using Linear Regression, LSTM and Decision Tree},
  howpublished = {EasyChair Preprint no. 7805},

  year = {EasyChair, 2022}}
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