Date of Defense
7-11-2025 9:30 AM
Location
Room 1012, E1 Building
Document Type
Thesis Defense
Degree Name
Master of Science in Information Technology Management
College
College of Information Technology
Department
Information Systems and Security
First Advisor
Prof. Amir Ahmad
Keywords
Information Technology Startups (SIT), Criteria, Startup Success Prediction, Entrepreneur, Capital Ventures, Policymakers, Series B, Machine Learning (ML), Feature, Classifier, Model, Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost).
Abstract
Lately, startups attracted significant attention from investors throughout the previous years. This raised several questions concerning startups and what they possibly define as them. It could refer to collective individuals who focus on innovative ideas with a reproducible and scalable business model; others refer to it as a newly established business. Nevertheless, all these definitions lead to a predictive question. Will these startups face success? This study explores startup success prediction methods, focusing on forecasting information technology startup (SIT) insights using Machine Learning (ML) models such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). Benefiting founders, investors, capital ventures & policymakers. These research findings will point to several startup prediction enhancement methods to increase success probabilities. Startups are crucial for innovation support, community productivity, and economic growth. However, newly established startups face one of the most controversial challenges, which is existence. The research aim is to point arrows at main success parameters to overcome this challenge. With the help of previously conducted research studies. Analysis will be implemented, presenting essential features such as funding amount, funding status, operating status, and number of founders. Additionally, suitable criteria, such as achieving a merger and acquisition or reaching Series B funding, will be considered. Effective classifiers for productively forecasting SIT success insights will also be explored. Furthermore, key solutions to previous challenges and effective characteristics contributing to startup success are identified through a literature survey.
Included in
STARTUP SUCCESS FORECASTING THROUGH MACHINE LEARNING: A COMPREHENSIVE ANALYSIS OF IT STARTUPS
Room 1012, E1 Building
Lately, startups attracted significant attention from investors throughout the previous years. This raised several questions concerning startups and what they possibly define as them. It could refer to collective individuals who focus on innovative ideas with a reproducible and scalable business model; others refer to it as a newly established business. Nevertheless, all these definitions lead to a predictive question. Will these startups face success? This study explores startup success prediction methods, focusing on forecasting information technology startup (SIT) insights using Machine Learning (ML) models such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). Benefiting founders, investors, capital ventures & policymakers. These research findings will point to several startup prediction enhancement methods to increase success probabilities. Startups are crucial for innovation support, community productivity, and economic growth. However, newly established startups face one of the most controversial challenges, which is existence. The research aim is to point arrows at main success parameters to overcome this challenge. With the help of previously conducted research studies. Analysis will be implemented, presenting essential features such as funding amount, funding status, operating status, and number of founders. Additionally, suitable criteria, such as achieving a merger and acquisition or reaching Series B funding, will be considered. Effective classifiers for productively forecasting SIT success insights will also be explored. Furthermore, key solutions to previous challenges and effective characteristics contributing to startup success are identified through a literature survey.