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SMIP: Stock Market Index Prediction Using Linked Open Data

Supervised By:
Imran Ihsan
Duration:
2020 - 2021
Client:
Team:
Completed:
Project completed in 34%.
Initial Design

Project description

A Regression problem for stock market index prediction using Twitter and Linked Open Data

Our Project is Mainly based on Semantic algorithms and machine learning Principles to find correlation between public mood and stock market index, According to most researches stock market is difficult to predict most of the time stock market index depend upon public mood so for this purpose we need public mood analysis with help of twitter tweets The Efficient Market Hypothesis states that stock market indexes are largely driven by new information and follow a Random walk The technique we use in this project is based on Bollen et al. The Raw (Stock Market) data fed into the pre-processor to obtain the processed values. At the same time we use Semantic algorithms for public mood analysis after this we fed all our value

Tools and Technologies

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