1) Train a supervised model to predict the stock prices of a certain company by using all the stock data available from the past few years. A method called a "similar day approach" can be used to to train such a system. Time series prediction may be used for this.
2) Build a predictor that predicts the best stock to invest into based on the following
i) The recommendation of experts (the weighted mean where weight of each expert is the rating he obtains)
ii) The performance of the company in the past few months (Time series)
iii) Return prediction from the previously trained model
3) Use natural language processing to automatically redirect a user to relevant information by looking at his/her queries, past searches etc.
4) Predict the effect of various day to day happenings or events on the stock market prices. For example, If donald trump wins elections, will that effect the prices of particular stocks?
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2) Build a predictor that predicts the best stock to invest into based on the following
i) The recommendation of experts (the weighted mean where weight of each expert is the rating he obtains)
ii) The performance of the company in the past few months (Time series)
iii) Return prediction from the previously trained model
3) Use natural language processing to automatically redirect a user to relevant information by looking at his/her queries, past searches etc.
4) Predict the effect of various day to day happenings or events on the stock market prices. For example, If donald trump wins elections, will that effect the prices of particular stocks?
Best regards,
Chaitanya
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