Stock Trend Prediction

Abstract

The rapid evolution of financial markets demands advanced tools and methodologies for effective decision-making. This project explores the area of stock trend prediction by leveraging the power of deep learning techniques. It is aimed at developing a reliable and nearby forecast model that will help Bulk Investors, Business people, Retail investors and Economic analysts to make some decisions. Our approach involves the utilization of deep learning algorithms, specifically neural networks, to analyze historical stock market data and extract meaningful patterns. To improve the accuracy and make effective model we also add some calculated features to the model. The project employs a comprehensive dataset encompassing diverse financial indicators and historical stock prices to train the neural network model. Key components of the project include data preprocessing, feature selection, model architecture design and training/validation strategies. Evaluation will be done by testing on seen data.