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Ai Or Machine Learning By Analysis Big Data Vector Image The aims of this study are to predict the stock price trend in the stock market in an emerging economy. A vector autoregression (var) based rolling prediction model is proposed for forecasting stock prices, and a gaussian feed forward neural networks (gfnn) based graphic signal identification method is introduced to recognize different types of stock price signals. This study examines the effectiveness of algorithms like decision trees, random forests, support vector machines (svm) with different kernels, and k means clustering using a dataset of tesla stock transactions. This paper provides a comparative analysis between three machine learning algorithms—linear regression, support vector machines (svm), and random forest as tools to perform predictions on. This paper will focus on applying machine learning algorithms like random forest, support vector machine, knn and logistic regression on datasets. we evaluate the algorithms by finding performance metrics like accuracy, recall, precision and f score. Stock price prediction remains a prominent area of interest among investors due to its potential impact on financial decision making. we developed a deep learning based system for stock market analysis, forecasting, and automated trading. utilizing historical financial data, technical indicators, and sentiment information, long short term memory (lstm) networks were employed to model and.
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