" Stock Market Prediction using Deep Learning"

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Prof. Yogita More, Ms. Sharwaree Garad, Ms. Shraddha Gavali, Ms. Srushti Nawale, Ms. Pradnya Thite


Investing in a diverse range of assets has always posed challenges due to the inherent unpredictability of financial markets. Simple models often struggle to accurately forecast future asset values given the complexities of market dynamics. Machine learning, a field dedicated to empowering computers to perform tasks requiring human-like intelligence, stands at the forefront of contemporary scientific research. This article aims to leverage Deep Learning, particularly the Long-Short Term Memory (LSTM) model, to predict future stock market values. The primary objective of this research is to evaluate the precision of machine learning systems in stock market prediction, a task traditionally demanding significant collaboration between humans and computers. The proposed LSTM model is anticipated to yield more precise predictions compared to existing stock price forecasting systems. Through training and testing the network with datasets of varying sizes, the study endeavors to provide a comprehensive analysis of its accuracy. Ultimately, the study's aim is to enhance investment decision-making by forecasting stock market prices with greater insight and precision.

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