Implementation of cross industry standard process for data mining (crisp-dm) in finance industry for stock market performance analysis and prediction
This thesis simulates a prototype development of a real world Data Mining application for stock analysis and future price prediction. The whole project was carried through according to the CRISP-DM methodology processes. rnCRISP-DM is the standard to develop data mining project. It is a detailed and widely used data mining methodology that aims to provide explicit guidance regarding how the various phases of a data mining project could be executed.rnData Mining is an analytic process designed to explore data typically used for finance industry in search of consistent patterns and systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. The ultimate goal of data mining is prediction, which is also the most common type of data mining. Data mining and forecasting are done through statistic methods; some particular methods will be used this thesis.rnBased on the research conducted, it can be concluded that CRISP-DM is the most appropriate methodology to develop a data mining project because of its extensive and efficient processes.
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