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Indian Start-ups’ Success Prediction Using Machine Learning

Balu, Nixon (2023) Indian Start-ups’ Success Prediction Using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Entrepreneurship is essential to the growth of the economy. Their success generates international investment and expands job opportunities. Investors stand to gain significantly from the success of these high-risk businesses, which in turn enables them to support further start-ups and maintain the economic growth cycle. In order to create a trustworthy and impartial model to predict the performance of such start-ups in the Indian market, this research introduces a novel approach in the feature engineering space. Furthermore, two experiments with different sampling techniques are designed, one of which uses data from start-ups only in India and the other of which includes start-ups from all over the world. These models are assessed with the state-of-the-art evaluation metrics used in this domain. RandomForest with weighted balance sampling technique demonstrated to be the most efficient model having significant and consistent F1, recall and accuracy scores.

Item Type: Thesis (Masters)
Horn, Christian
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
H Social Sciences > HD Industries. Land use. Labor > New Business Enterprises
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 17 May 2023 10:19
Last Modified: 17 May 2023 10:19

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