[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121032-en":3,"doc-seo-121032-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121032,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Machine Learning Techniques For Stock Price Prediction - A Comparative Analysis Of Linear Regression, Random Forest, And Support Vector Regression","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.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 45 IssueS-4 Year 2024 Page 118-127  \nMachine Learning Techniques For Stock Price Prediction-A Comparative Analysis Of Linear Regression, Random Forest, And Support Vector Regression  \nShital Sameer Pashankar1*, Jyoti Dashrath Shendage2, Dr. Janardan Pawar3  \n1 *Computer Science, Indira College of Commerce and Science, Pune, India.  \nEmail: [srbhigwankar@gmail.com](srbhigwankar@gmail.com)  \n2Computer Science, Indira College of Commerce and Science, Pune, India.  \nEmail: [shendagejyoti@gmail.com](shendagejyoti@gmail.com)  \n3Computer Application, Indira College of Commerce and Science, Pune, India.  \n[Email: janardanp@iccs.ac.in](Email: janardanp@iccs.ac.in)  \n*Corresponding Author: Shital Sameer Pashankar  \nEmail: [srbhigwankar@gmail.com](srbhigwankar@gmail.com)  \n\n| CC License\u003Cbr>CC-BY-NC-SA 4.0 | Abstract\u003Cbr>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.\u003Cbr>Keywords: Stock market, Linear Regression, Random Forest, Support Vector Regression (SVR), Prediction. |\n| --- | --- |\n\nINTRODUCTION  \nThe 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.  \nIn 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","cbCaiqBH4I6GUtaa","https://ap.wps.com/l/cbCaiqBH4I6GUtaa","pdf",783338,1,10,"English","en",105,"# Introduction\n## Limitations of Traditional Stock Forecasting\n## Role of Machine Learning in Stock Prediction\n## Model Descriptions: Linear Regression, Random Forest, SVR","[{\"question\":\"Which machine learning models are used to predict TCS stock prices?\",\"answer\":\"The study uses linear regression (LR), support vector regression (SVR), and random forest models to predict Tata Consultancy Services (TCS) stock prices.\"},{\"question\":\"How are the models evaluated in this study?\",\"answer\":\"Evaluation relies on time-series comparisons using standard metrics Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).\"},{\"question\":\"What improvement is reported after hyperparameter tuning?\",\"answer\":\"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.\"}]","Machine Learning Techniques For Stock Price Prediction - A Comparative Analysis Of Linear Regression, Random Forest, And Support Vector Regression | PDF",1785733405,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-techniques-for-stock-price-prediction-a-comparative-analysis-of-linear-regression-random-forest-and-support-vector-regression","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-techniques-for-stock-price-prediction-a-comparative-analysis-of-linear-regression-random-forest-and-support-vector-regression/121032/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning models are used to predict TCS stock prices?","Question",{"text":75,"@type":76},"The study uses linear regression (LR), support vector regression (SVR), and random forest models to predict Tata Consultancy Services (TCS) stock prices.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the models evaluated in this study?",{"text":80,"@type":76},"Evaluation relies on time-series comparisons using standard metrics Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).",{"name":82,"@type":73,"acceptedAnswer":83},"What improvement is reported after hyperparameter tuning?",{"text":84,"@type":76},"After hyperparameter tuning for SVR and random forest, RMSE decreases, indicating a lower error rate. 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