Prediction of stock prices of blue-chip companies using machine learning algorithms

Accurate stock market prediction is a very challenging task for experts due to its volatile nature. To determine the future value of the stock market, several researches are based on historical data. But nowadays, there are some external factors like social media and news headlines that greatly affect the stock market. This research work is based on the prediction of future stock prices by using both twitter social media and news data along with historical data to get the high prediction results. The performance of machine learning algorithms - logistic regression, SVM, random forest is analysed using matrices like accuracy, precision, recall, and F1 score. To train and test the final dataset, it is divided into 80:20 ratios. For each blue chip company, the testing dataset contains 248 samples, which exhibited the highest prediction accuracies ranging from 85% to 89% for prediction of stock prices is achieved using logistic regression algorithm.