• Abijith Sharma V S

ABSTRACT The science of getting computers to act without being explicitly programmed is termed as Machine Learning. Machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. In this project, the method to predict stock market values using machine learning algorithms: Linear Regression, Random forest and Multilayer Perceptron. In statistics, linear regression is a linear approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variables) denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of over fitting to their training set. Here in this project, the stock market values are predicted by implementing Artificial Neural Network by using Sentimental Analysis. Sentimental analysis can better predict the markets compared to individual analysis that is currently employed by Industry today.

"Stock Market Prediction using Multivariable Neural Network combined with Sentimental Analysis. ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.248-251, June 2019, Available :http://www.jetir.org/papers/JETIR1906M34.pdf

"Stock Market Prediction using Multivariable Neural Network combined with Sentimental Analysis. ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp248-251, June 2019, Available at : http://www.jetir.org/papers/JETIR1906M34.pdf