Browsing by Author "Qureshi, Mohammed Usman (19CO47)"

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    Stock analysis and prediction website
    (AIKTC, 2023-05) Mishra, Richa; Shaikh, Muhammad Huzaifa (18CO40); Bagdadi, Ameeruddin (19CO12); Khan, Raquib (19CO31); Qureshi, Mohammed Usman (19CO47)
    In this project we attempt to implement machine learning approach to predict stock prices. Machine learning is effectively implemented in forecasting stock prices. The objective is to predict the stock prices in order to make more informed and accurate investment decisions. We propose a stock price prediction system that integrates mathematical functions, machine learning, and other external factors for the purpose of achieving better stock prediction accuracy and issuing profitable trades. There are two types of stocks. You may know of intraday trading by the commonly used term ”day trading.” Interday traders hold securities positions from at least one day to the next and often for several days to weeks or months. LSTMs are very powerful in sequence prediction problems because they’re able to store past information. This is important in our case because the previous price of a stock is crucial in predicting its future price. While predicting the actual price of a stock is an uphill climb, we can build a model that will predict whether the price will go up or down. Apart from that, we have included a stock analysis feature, which helps users to understand the performance of a particular stock, also it guides users to invest in a stock whose price would eventually rise up in the coming future, leading to profit gains.