The problem StockBasket solves

Predicting how the stock market will perform is one of the most challenging things to do. There are so many factors involved in the prediction. We can divide this into physical factors and psychological, rational and irrational behaviour, etc. All these aspects combine to make share prices volatile and very difficult to predict with a high degree of accuracy. Using features like the latest announcements about an organization, their quarterly revenue results, and other machine learning techniques have the potential to unearth patterns and insights we didn’t see before. These can be used to make accurate predictions. Implementation of a mix machine learning algorithms to predict the future stock price of the company, starting with simple algorithms like averaging and linear regression, and then move on to advanced techniques like the random forest to predict the stock for next 5 days as accurate as possible.

Challenges I ran into

• Getting a proper data set of Indian financial news for sentiment analysis through news was a struggle. We overcome this challenge by taking DJIA (Dow Jones Industrial Average ) financial news dataset to train our model to classify news based on their effect on the stock market for that particular company.
• To predict stock prices for the next five days with a moving average using the most suitable algorithm was a challenge.