STOCK MARKET PREDICTION AND EFFICIENCY ANALYSIS

Abstract

Stock is an unpredictable curve. Prediction in the stock market is covered with complexity and instability. The main aim for the persuasion of the topic is to predict the stability in the future market stocks. Many researchers have performed their research on the movement of future market evolution. In the recent trend of Stock Market Prediction Technologies machine learning has integrated itself in the picture for deployment and prediction of training sets and data models. Machine Learning employs different predictive models and algorithms to predict and automate things of requirement. We are using the Nifty 50 dataset for our project. It consists of six attributes and 4000 records spanning from the year 2004 till 2020. For the implementation of our project we are utilizing the machine learning techniques such as Linear Regression, Polynomial Regression, Knn algorithm and Decision tree. Performance of the algorithms will be measured using precision accuracy.