Stock Price Analysis and Forecasting Using Linear Regression and SVM Classifiers
Keywords:
data set, deep network, Linear Regression, machine Learning, SVM, stock modellingAbstract
In this study, we attempt to implement a Machine Learning approach to predict stock market prices. Linear Regression is very effectively implemented in forecasting stock prices, returns, and stock modelling. This project is for common users as the prediction is done on all of the companies. We outline the design of the Linear Regression model with its salient features and customizable parameters. We select a certain group of parameters with a relatively significant impact on the share price of a company. With the help of analysis, the relation between the selected factors and share price is formulated which can help in forecasting accurate results. Although, share market can never be predicted, due to its vague domain, this project aims at applying Machine Learning in forecasting the stock prices.
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