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Visualizing And Forecasting Stocks Using Single Page Application

M. John Manohar, K. Bharath Reddy, Md. Fayazuddin, Venkata Rao Tavanam


In the fields of finance and economics, stock price forecasting is a vital and crucial subject. The stock market is not governed by any significant rules that may be used to anticipate or estimate the price of a stock. In an effort to forecast the price in the stock market, several techniques are employed, including technical analysis, fundamental analysis, time series analysis, statistical analysis, etc. However, none of these techniques has been consistently demonstrated to be an effective prediction tool. We attempt to implement, forecast and analyze stock market prices in this paper. With breakthroughs in the fields of computer and artificial intelligence, neural networks based on machine learning techniques are becoming more effective for stock price prediction and modelling. The relationship between the selected elements and share price is formed using the dash library in Python, which can aid in anticipating accurate outcomes. Despite the fact that the stock market can never be precisely forecasted owing to its unpredictability, the purpose of this project is to use the notion of prediction and analyze data in order to forecast stocks.


“ML”, “python”, “prediction”, “stocks”, “SPA”

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