Stock price prediction methodology using random forest algorithm and support vector machine

Abstract

Stock Market Prediction provides wide area of Research which is revenue for the country and useful for day-to-day analysis. A lot of financial specialists performs predictive Analysis and finds the Market trends for their business. Analysts uses this Prediction for long time because of its unpredictable, composite, and consistently varying in character which was very hard to build solid expectations. This work proposes a methodology towards the expectation of pattern matching using AI methods like Random Forest and Support Vector Machine (SVM). The Random Forest method is a group learning strategy which is an extremely effective method for order & relapse. Support vector mechanism is an AI representation for the order and this model is generally utilized for arrangement. These procedures are utilized to determine whether the cost of stock will be higher than its cost on a given day to make profitable trading strategies. The main aim of this work is to create and evaluate a stock price predictor to make profitable trading strategies using AI and Data mining approaches.