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Recently, researchers intensified their efforts on a dataset with a large number of features named Big Data because of the technological revolution and the development in the data science sector. Dimensionality reduction technology has efficient, effective, and influential methods for analyzing this data, which contains many variables. The importance of Dimensionality Reduction technology lies in several fields, including "data processing, patterns recognition, machine learning, and data mining". This paper compares two essential methods of dimensionality reduction, Feature Extraction and Feature Selection Which Machine Learning models frequently employ. We applied many classifiers like (Support vector machines, k-nearest neighbors, Decision tree, and Naive Bayes) to the data of the anthropometric survey of US Army personnel (ANSUR 2) to classify the data and test the relevance of features by predicting a specific feature in USA Army personnel results showing that (k-nearest neighbors) achieved high accuracy (83%) in prediction, then reducing the dimensions by several techniques like (Highly Correlated Filter, Recursive Feature Elimination, and principal components Analysis) results showing that (Recursive Feature Elimination) have the best accuracy by (66%), From these results, it is clear that the efficiency of dimension reduction techniques varies according to the nature of the data. Some techniques are more efficient than others in text data and others are more efficient in dealing with images. [ABSTRACT FROM AUTHOR]

Stock prediction is one of the most important issues on which the investor relies in building his investment decisions and the financial literature has relied heavily on predicting future events because of its exceptional importance in financial work, after which profit or loss is determined, and since money dealers are eager to profit, the researchers have devoted techniques to forecast as providing the tools to achieve this. The choice of the proper model of time series data affects the precision of the predictions, and stock market data is typically random and turbulent for various industries. To obtain forecast models of stock market data that can accurately portray reality and obtain future forecasts, these models must take all data considerations from linear and none linear trends, different influences, and other data factors, hence the research problem of obtaining a method that gives predictions of Iraq's stock market indicators that are accurate and reliable in stock analysis. In this paper, two models were proposed to predict the Iraqi stock markets index through the use of artificial neural networks (ANN) and a long short-term memory (LSTM) algorithm where Iraqi stock market data were used from 2017 to 2021 and good results were achieved in the prediction where the long short-term memory (LSTM) algorithm reached a mean square error (MSE) rate of as little as 0.0016 while the artificial neural network (ANN) algorithm reached error rate 0.0055. [ABSTRACT FROM AUTHOR]