A Cross-Validated Comparative Analysis of Machine Learning Models for Strategic Decision- Making in Software Startups

In the constantly changing landscape of software startups, data-driven approaches have been essential in overcoming challenges in the form of limited resources and high failure rates. The research examines the performance of machine learning (ML) models to forecast key outcomes like customer retention, revenue growth, and product success. The main aim is to determine effective ML models that aid decision-making in the context of constraints experienced by startups. Using a crossvalidated comparative method, the study leverages a dataset with a higher number of records and key features spanning financial, operational, and market-based aspects. Five ML modelsLogistic Regression, Random Forest, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), and XGBoost-were compared using 10 -fold cross-validation to assess their accuracy, reliability, and responsiveness. Rigorous data preprocessing in the form of feature selection, encoding, and scaling ensured optimal model performance. Among the models, Random Forest was better at 91.67%, followed by SVC and XGBoost. Team expertise, focused funding allocation, and comprehensive market analysis are key success factors. The beneficiaries of this study are startup founders, investors, and other stakeholders interested in practical, data-driven solutions to enable strategic planning. With the use of ML tools, startups can improve adaptability and competitiveness in fast-changing market scenarios. This study makes a noteworthy contribution to the use of ML in software startups by providing a structured framework to utilize predictive analytics for growth and sustainability.