[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118851-en":3,"doc-seo-118851-105":30,"detail-sidebar-cat-0-en-105":83},{"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},118851,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Assessing Machine Learning's Accuracy in Stock Price Prediction - Research Study","This research examines how effectively machine learning models predict the closing price of traded stocks, reflecting the growing adoption of these techniques in finance. The study compares Linear Regression, Random Forest, and K Nearest Neighbor (KNN) to identify which models deliver the most accurate forecasts and why. A dataset built from five stocks across sectors is used, with modeling and analysis performed in Python using Pandas, NumPy, Matplotlib, and scikit-learn. Performance is evaluated using Mean Squared Error (MSE).","Assessing Machine Learning's Accuracy in Stock Price  \nPrediction  \nAryan Bhattaa*, Pranshu Poudyalb, Drishant Kumar Maharjanc, Aryaa Thapad  \na,b,c,dPremier International School, Kathmandu, Nepal  \ndLancers International School, Gurgaon, India  \naEmail: [aryan.b11@premier.edu.np](aryan.b11@premier.edu.np)  \nAbstract  \nThis research examines how well machine learning models can predict the closing price of traded stocks. The financial industry has seen an increase in the use of these models due to the availability of datasets and technological advancements. The study compares machine learning models such as Linear Regression, Random Forest and K Nearest Neighbor (KNN) to determine which ones are the accurate predictors and what factors contribute to their effectiveness. To gain insights into model performance a diverse dataset consisting of five stocks from sectors is used. Data analysis and modelling are conducted using Python programming language with libraries, like Pandas, NumPy, Matplotlib and Scikit learn. The performance evaluation metric utilized is Mean Squared Error (MSE) . The research findings have the potential to assist investors and traders in making decisions while also contributing to the growth of the financial industry.  \nKeywords: Machine Learning; Stock Price Prediction; Linear Regression; Random Forest K Nearest Neighbor (KNN); Mean Squared Error (MSE); Financial Industry.  \n1. Introduction  \nPredicting the closing price of traded stocks in the market has been popularized by the emergence of machine learning models that offer comprehensive features and tools for this problem. These models have become more accessible due to advances in technology, the availability of vast amounts of historical data, and the need for accurate stock price predictions in the financial industry. As a result, both retail investors and companies are using machine learning models to gain insight into future stock prices. This research will focus on the effectiveness of machine learning models commonly used to predict the closing price of stocks. Several machine learning models are available for this task. each with advantages and disadvantages. This investigation aims to compare and contrast these models in order to identify the most effective ones and the factors contributing to their effectiveness.  \nReceived: 8/7/2023  \nAccepted: 9/12/2023  \nPublished: 9/26/2023  \n* Corresponding author.  \nFurthermore, the relevance of this research goes beyond individual or organizational levels due to the intersection of the country's financial sector and the use of machine learning.  \nAccurate stock price forecasts are essential for investment decisions and can significantly affect a country's economic growth. Therefore, this research can influence foreign investment decisions, profits and losses. Understanding the effectiveness of various machine learning models in predicting stock prices can inform investment decisions and contribute to the growth of the financial industry.  \n1.1 Background Information  \nThe closing cost of a stock is a key factor in determining investment trends for both investors and traders. This figure shows the final market rate that stock holds during a given day, as well as how much it costs to purchase a single share. There are numerous external elements that have the potential to drastically alter stock price, such as economic conditions, information, company efficiency, and global activity. Since these variabilities are harder to foresee, stocks normally undergo heightened fluxes.  \nUtilizing machine learning to select stocks has grown in popularity lately. It can make use of expansive datasets to find patterns that the typical individual may not notice. This has been used to generate multiple machine learning algorithms that are specifically made to estimate stock expenses. They can be prepared by utilizing past data in order to anticipate stock prices in the future.  \nAs machine learning models evolve, they ha","cbCaivbVA1dt7hWQ","https://ap.wps.com/l/cbCaivbVA1dt7hWQ","pdf",902592,1,18,"English","en",105,"# Introduction\n## Background Information\n## Supervised and Unsupervised Learning Models","[{\"question\":\"How is model performance measured?\",\"answer\":\"Model performance is assessed using Mean Squared Error (MSE). 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