Identification of Survey Fraudulent Data

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2019
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This project tackles the challenge of fraudulent data in surveys. Businesses often rely on surveys for product development and research, making data authenticity crucial. However, fraudulent entries can be introduced by respondents aiming to save time or gain an advantage. Manually identifying such entries within large datasets is a significant burden. Our project proposes a working model to automatically detect fraudulent survey data. This will optimize the survey process by minimizing the presence of inaccurate information, leading to more reliable results.
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Electrical and Computer Engineering
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North South University
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