  Predicting the turnover of a company in the ever fluctuating Stock market has
always proved to be a precarious situation and most certainly a difficult task
in hand. Data mining is a well-known sphere of Computer Science that aims on
extracting meaningful information from large databases. However, despite the
existence of many algorithms for the purpose of predicting the future trends,
their efficiency is questionable as their predictions suffer from a high error
rate. The objective of this paper is to investigate various classification
algorithms to predict the turnover of different companies based on the Stock
price. The authorized dataset for predicting the turnover was taken from
www.bsc.com and included the stock market values of various companies over the
past 10 years. The algorithms were investigated using the "R" tool. The feature
selection algorithm, Boruta, was run on this dataset to extract the important
and influential features for classification. With these extracted features, the
Total Turnover of the company was predicted using various classification
algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression.
This prediction mechanism was implemented to predict the turnover of a company
on an everyday basis and hence could help navigate through dubious stock market
trades. An accuracy rate of 95% was achieved by the above prediction process.
Moreover, the importance of stock market attributes was established as well.
