Exploring Startup Registration Trends in India: A Comprehensive Analysis and Regression Modeling with Scikit-Learn

Shete, Pratik (2024) Exploring Startup Registration Trends in India: A Comprehensive Analysis and Regression Modeling with Scikit-Learn. Masters thesis, Dublin, National College of Ireland.

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Abstract

This in-depth analysis uses large registration data to explore the dynamic and constantly changing startup environment in India. By using rigorous data pretreatment, perceptive exploratory analysis, and sophisticated predictive modeling, we reveal a wealth of information about sector diversification, geographic dispersion, and the significant influence of foreign investment. We can observe that our models work exceptionally well: the Random Forest Regressor has an R-squared score of 0.96, the Gradient Boosting Regressor has an amazing score of 0.94, and the Bagging Regressor improves predictive accuracy considerably. Our research highlights the entrepreneurial energy fostering innovation and economic expansion in a variety of industries and geographical areas. Our report equips investors, entrepreneurs, and legislators with the necessary knowledge to see patterns, take advantage of chances, and confidently negotiate the intricacies of the startup environment.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Shahid, Abdul UNSPECIFIED
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:	Ciara O'Brien
Date Deposited:	05 Jun 2025 15:25
Last Modified:	05 Jun 2025 15:25
URI:	https://norma.ncirl.ie/id/eprint/7771

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