Stock price prediction using data analytics

Accurate financial prediction is of great interest for investors. This paper proposes use of Data analytics to be used in assist with investors for making right financial prediction so that right decision on investment can be taken by Investors. Two platforms are used for operation: Python and R. various techniques like Arima, Holt winters, Neural networks (Feed forward and Multi-layer perceptron), linear regression and time series are implemented to forecast the opening index price performance in R. While in python Multi-layer perceptron and support vector regression are implemented for forecasting Nifty 50 stock price and also sentiment analysis of the stock was done using recent tweets on Twitter. Nifty 50 (
^A
NSEI) stock indices is considered as a data input for methods which are implemented. 9 years of data is used. The accuracy was calculated using 2-3 years of forecast results of R and 2 months of forecast results of Python after comparing with the actual price of the stocks. Mean squared error and other error parameters for every prediction system were calculated and it is found that feed forward network only produces 1.81598342% error when opening price of stock is forecasted using it.