It has been proved that the decisions in stock market exchange may bring influence on the investors, financial institutions, banking sectors etc. The stock market is a highly composite system in addition often concealed with mystery, it is therefore, very difficult to analyze all the impacting factors before making a decision. In this research, we have tried to design a stock market prediction model which is considers different parameters of a particular stock. Analysis is performed after obtaining the stock scores. This analysis involves visualization of stock scores in the form of various plots and prediction of the scores using a time series model known as ARIMA (auto regressive moving average). The results shows that the time series model performed a descent prediction of the market scores with considerably high accuracy. Each factor was studied independently to find out its association with market performance. Furthermore the results suggests that behavior of market can be predicted using machine learning techniques. © 2018 IEEE.