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    [CS297 Proposal]





    [CS 297 Report-PDF]

    [CS298 Proposal]

    [CS 298 Report-PDF]

    [CS 298 Presentation-PDF]


In this deliverable, I have developed a program to predict the closing price of a stock using the historical data of the same stock. For example, stock data for CISCO Systems is given from the year 2010 till today. Based on this data, a program has to predict what will be the closing price of CISCO today.

In stock market prediction, we predict the price of a particular stock. As price is a continuous variable, the regression method works better. Because if we compare to stock trends, the exact increment in stock index may provide more information for building a prediction model.

And similarly, in general humans tend to do similar trading. They try to buy or sell the stock based on the past performance of the stock. People change their strategy only when there is some major current news that influences the stock market. For this problem, I used the Decision Tree and K Nearest Neighbors (KNN) machine learning techniques, because they can capture the past data and make a reliable prediction based on it.

To run the program: Type $ python ./