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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Research Article

Comparative Study of Machine Learning Algorithms towards Predictive Analytics

Author(s): Maheswari Petchiappan* and Jaya Aravindhen

Volume 16, Issue 6, 2023

Published on: 17 October, 2022

Article ID: e230622206361 Pages: 11

DOI: 10.2174/2666255816666220623160821

Price: $65

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Abstract

Background: The trend of the stock market prediction has always been challenging and confusing for investors. There is tremendous growth in stock market prediction with the advancement of technology, machine learning, data science, and big data. The media and entertainment sector is one of the diverse sectors in the stock market. In the Indian stock market, Sensex and Nifty are the two indexes. The 2019 pandemic forced the movie theatres to shut down. As a result, distributors and film directors were not able to release their movies in theatres, and production was also stopped. Consequently, during the lockdown, people spent more time at home watching electronic media, resulting in a higher degree of media consumption.

Objectives: The objective of the research is to predict the performance of the media and entertainment companies stock prices using machine-learning techniques. Investors will be benefited by maximizing the profit and minimizing the loss.

Methods: The proposed stock prediction system is used to predict the stock values and find the accuracy of linear regression and logistic regression in machine learning algorithms for data science.

Results: The experiments are conducted for the media and entertainment stock price data using Machine-learning algorithms. Media stock prices are considered as the input dataset. The model has been developed using the daily frequency of stock prices with different attributes.

Conclusion: Thus, the media and entertainment stocks are predicted using linear regression and logistic regression. Using the above techniques, stock prices are predicted accurately to maximize profits and minimize the loss for the investors.

Keywords: Stock market, prediction, machine learning, linear regression, logistic regression, supervised learning, and data science.

Graphical Abstract

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