Stock Market Prediction Using Machine Learning In Python
Nikhil Patil, Sanket Kulkarni, Mahesh Kulkarni, Piyush Nankar, Dinesh Kulkarn
LSTM, Linear Regression, Supervised Learning, Unsupervised Learning, Stock
Expectations on securities exchange costs are an extraordinary test because of the way that it is a tremendously mind-boggling, tumultuous and dynamic condition. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article considers the use of LSTM arranges on that situation, to foresee future patterns of stock costs dependent on the value history, nearby with specialized examination pointers. For that goal, a prediction model was built, and a series of experiments were executed and their results analyzed against a number of metrics to assess in the event that this kind of calculation presents and enhancements when contrasted with other Machine Learning techniques and venture methodologies. The results that were obtained are promising, getting up to an average of 55.9% of accuracy when predicting if the price of a particular stock is going to go up or not in the near future.
Article Details
Unique Paper ID: 148588

Publication Volume & Issue: Volume 6, Issue 3

Page(s): 167 - 171
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