Comparison with Deep Learning Methods For
Predicting Stock Prices

Comparison with Deep Learning Methods For
Predicting Stock Prices

Stock price, Linear Regression, Support Vector
Machine (SVM), K-Nearest Neighbors (KNN

Recently, machine learning has been an essential tool
for analysis in diverse fields, including science, sports
management, and economics. In particular, the stock market
comprises a complex network of buyers and sellers engaged in
stock trading. So, predicting stock prices has been developed
using machine-learning techniques to significantly enhance such
forecasts' accuracy. Recent advancements have improved the
performance of several algorithms, such as Linear Regression,
Support Vector Machines (SVM), and K-nearest neighbors
(KNN) to predict stock prices. Stock price datasets typically
contain information such as opening and closing prices, high and
low values, dates, trading volume, and adjusted closing prices
provided by Yahoo Finance. Based on the data, this research
evaluates the prediction accuracy of each machine-learning
method and presents the results through data visualizations,
including box plots and tables. The compiled results will assist in
identifying the most effective model for stock price prediction.

Colton Nutter, Nayeong Kong, Seonguk Kim

https://ijcit.com/index.php/ijcit/article/view/532

Division of Natural Science, Applied Science, and Mathematics
Defiance College
Defiance, OH 43512, USA

september 2025

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