Machine Learning Techniques For Stock Price Prediction - A Comparative Analysis Of Linear Regression, Random Forest, And Support Vector Regression

Aug 3, 2026

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Stock price prediction in a volatile market demands accurate, timely forecasts for both companies and investors. Machine learning models can learn complex relationships from large datasets and generate insights that are difficult to capture through manual analysis. This study applies linear regression, support vector regression, and random forest to predict Tata Consultancy Services (TCS) stock prices using feature engineering and hyperparameter tuning. Model effectiveness is assessed with MSE and RMSE on time-series data.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Limitations of Traditional Stock Forecasting

Role of Machine Learning in Stock Prediction

Model Descriptions: Linear Regression, Random Forest, SVR

Journal of Advanced Zoology ISSN: 0253-7214 Volume 45 IssueS-4 Year 2024 Page 118-127 Machine Learning Techniques For Stock Price Prediction-A Comparative Analysis Of Linear Regression, Random Forest, And Support Vector Regression Shital Sameer Pashankar1*, Jyoti Dashrath Shendage2, Dr. Janardan Pawar3 1 *Computer Science, Indira College of Commerce and Science, Pune, India. Email: [srbhigwankar@gmail.com](srbhigwankar@gmail.com) 2Computer Science, Indira College of Commerce and Science, Pune, India. Email: [shendagejyoti@gmail.com](shendagejyoti@gmail.com) 3Computer Application, Indira College of Commerce and Science, Pune, India. [Email: janardanp@iccs.ac.in](Email: janardanp@iccs.ac.in) *Corresponding Author: Shital Sameer Pashankar Email: [srbhigwankar@gmail.com](srbhigwankar@gmail.com) | CC License
CC-BY-NC-SA 4.0 | Abstract
In the dynamic and rapidly evolving stock market, the ability to generate accurate and timely predictions holds paramount significance for companies and investors alike. Machine learning algorithms can identify complex patterns in the stock market. Machine learning algorithms playa critical role in this situation, leveraging their capacity to analyze extensive datasets and provide valuable insights, thereby forecasting future trends. Stock price prediction is still an arduous task because of the financial markets' well-known volatility. In recent years, there has been a significant increase in the use of machine learning techniques for stock price prediction. This is because these algorithms can handle large amounts of data and identify complex patterns that are difficult for humans to recognize. The proposed methodology focuses on using linear regression (LR), support vector regression (SVR), and random forest machine learning models to predict Tata Consultancy Services (TCS) stock prices. Machine learning presents a promising method in this field of stock price prediction, which is essential for traders and investors to make well-informed judgements. via data on TCS stock prices, the study evaluates the models via feature engineering and hyperparameter tuning. An understanding of how well these algorithms anticipate stock values is given by the analysis and findings. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) standard metrics are used to compare the time series data. After Applying hyperparameter tuning on Support Vector Regression and Random Forest the standard metrics RMSE shows decrease in error rate. In the proposed model Linear Regression has better performance than Support Vector Regression and Random Forest.
Keywords: Stock market, Linear Regression, Random Forest, Support Vector Regression (SVR), Prediction. | | --- | --- | INTRODUCTION The linear regression model demonstrates the lowest RMSE, indicating better predictive performance compared to Random Forest and SVR. Random Forest follows closely, while SVR appears to struggle in capturing the complexity ofthe TCS stock price data. In the contemporary landscape characterized by technological advancements and enhanced computing capabilities, machine learning has become pervasive across various industries. The realm of stock price prediction has particularly witnessed a surge in the application of machine learning techniques. Basically, the stock market is one where there are buyers and sellers interested in buying stocks ofa certain company, and the prices of these stocks vary widely over time [1] . Traditional methods of stock price prediction, like technical and fundamental analysis, rely on historical trends and human judgment. Although effective to a certain extent, these methods exhibit limitations. For instance, technical analysis predominantly concentrates on price patterns and trends in the stock market. In contrast, machine learning algorithms encompass a broader spectrum offactors, concurrently analyzing historical data, real-time market data, and economic indicators. This c

FAQs

Which machine learning models are used to predict TCS stock prices?

The study uses linear regression (LR), support vector regression (SVR), and random forest models to predict Tata Consultancy Services (TCS) stock prices.

How are the models evaluated in this study?

Evaluation relies on time-series comparisons using standard metrics Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).

What improvement is reported after hyperparameter tuning?

After hyperparameter tuning for SVR and random forest, RMSE decreases, indicating a lower error rate. The study also reports LR performing better than both SVR and random forest.

Aug 3, 2026

Upload by Maeve