Author : Aarti Ghodke and Nilesh Alone

The main objective of this paper is to find the best model to predict the value of the stock market. During the process of considering various techniques and variables that must be taken into account, we found out that techniques like support vector machine is not suitable for these applications. In, this system we are going to present and review a more feasible method to predict the stock movement with higher accuracy. The first thing we have taken into account is the dataset of the stock market prices from previous year. The dataset was preprocessed and tuned up for real analysis. We also proposed the sentiment analysis system for the social media data for getting the information about the views of the users of the particular stocks. This will represent the overall ranking and the quality of the stocks in market. This also helps the user to get the knowledge about the stock market. Hence, our system will also focus on data preprocessing of the raw dataset. Secondly, after pre- processing the data, we will review the use of Naives Bayesianon the dataset and the outcomes it generates. In addition, the proposed system examines the use of the prediction system in real-world settings and issues associated with the accuracy of the overall values given. The system also presents a machine learning model to predict the longevity of stock in a competitive market. The successful prediction of the stock will be a great asset for the stock market institutions and will provide real-life solutions to the problems that stock investors face.

Keywords: Stock exchanges, prediction, statistics, sentiment analysis, machine learning, pattern recognition