Forecasting Nasdaq stock progressions using classification and deep learning techniques
Abstract
Stock Market prices have always been unpredictable resulting in a lot of risk for its investors. This proposal uses machine learning techniques (Decision Tree, Random Forest, Adaptive Boosting (Adaboost), eXtreme Support Vector Classifier (SVC), Logistic Regression and deep learning methods such as Long short-term memory (LSTM) to build modules that can be used to predict accurate stock prices reducing the chances of risk and increasing in gains. In this proposal the National Association of Securities Dealers Automatic Quotation System (NASDAQ) stock data is being used which has been extracted from Yahoo Finance to predict and analyze various Stock Progressions.
How to Cite This Article
Dr. Syed Shabbeer Ahmad, Dr. Krishna Prasad K (2023). Forecasting Nasdaq stock progressions using classification and deep learning techniques . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 4(5), 40-49. DOI: https://doi.org/10.54660/IJMRGE.2023.4.5.40-49