A predictive system based on structured and unstructured data to gather and tag overall mood about a company and use it to correlate and predict stock prices for the company
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The main goal of this research paper is to develop and enhance a model which predicts the base outcome the shares in the share market. The model is based on the inputs taken from the latest stock news, put it into the database then news will predict the upcoming fluctuations rather positive and negative flags. The model will predict whether the upcoming day status of the share market will be positive or negative. The model can be created by feeding in the past data to predict or forecast the future price. Stock Market prediction refers to understanding various aspects of the stock market that can influence the price of a stock and, based on these potential factors, we built a model to predict the stock’s price. We have focused on both on analytics and modeling to predict +ve and –ve mood about the companies and compareit with the stock price change for the companies to the correlate and predict the stock price. We are using Leapwork(a RPA tool) to scrap new articles about certain companies, APls for examples to gether stock price for certain companies and put in to a MS data factory. Also, there are some additions for example compare other RPAs to scrap news articles, etc. The purpose of MS data factory was to mine the stock data and transforms to the next level of prediction. Data processing is a crucial activity that allows every company to get expertise and make smarter decisions. Also used Power BI to built a data visualization layer from the extracted stock data.