Recursive Stock Price Prediction With Machine Learning And Web Scrapping For Specified Time Period

摘要

In the finance world inventory trading is one of the most necessary activities. Stock market prediction is an act of attempting to decide the future price of a stock other monetary instrument traded on a financial exchange. The technical and integral or the time sequence evaluation is used with the aid of most of the stockbrokers while making the inventory predictions. This paper explains the prediction of a stock using Machine Learning. The input parameters include -open, high, low, close rate, trading volume, Price to Earning Ratio, MA, MACD for more accuracy. The Machine Learning algorithm, Random Forest Regression has been implemented in Python programming language which is used to predict the stock market. The algorithm has been used on the historical stock data along with web- scraping technique that has been applied to catch current market data of the stock. The recursive training model take its predicted value as input to predict further long term future stock rates.